{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Emotion recognition using Emo-DB dataset and CNN trained in pytorch\n",
    "\n",
    "### Database: Emo-DB database (free) 7 emotions\n",
    "The data can be downloaded from http://emodb.bilderbar.info/index-1024.html\n",
    "\n",
    "Code of emotions\n",
    "\n",
    "W->Anger->Wut\n",
    "\n",
    "L->Boredom->Langeweile\n",
    "\n",
    "E->Disgust->Ekel\n",
    "\n",
    "A->Anxiety/Fear->Angst\n",
    "\n",
    "F->Happiness->Freude\n",
    "\n",
    "T->Sadness->Trauer\n",
    "\n",
    "N->Neutral\n",
    "\n",
    "\n",
    "![image.png](http://iis-projects.ee.ethz.ch/images/thumb/a/a6/Emotions-on-arousal-valence-space.jpg/450px-Emotions-on-arousal-valence-space.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "/home/camilo/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/home/camilo/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/home/camilo/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/home/camilo/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/home/camilo/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/home/camilo/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    }
   ],
   "source": [
    "import requests \n",
    "import zipfile\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as st\n",
    "import itertools\n",
    "import sys\n",
    "sys.path.append(\"../\")\n",
    "import time\n",
    "from plots_examples import plot_confusion_matrix, plot_ROC, plot_histogram\n",
    "\n",
    "\n",
    "# disvoice imports\n",
    "from phonation.phonation import Phonation\n",
    "from articulation.articulation import Articulation\n",
    "from prosody.prosody import Prosody\n",
    "from phonological.phonological import Phonological\n",
    "from replearning.replearning import RepLearning\n",
    "\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "\n",
    "# torch imports\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import torch.nn as nn\n",
    "import torch.utils.data as data\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "from sklearn import metrics\n",
    "from sklearn.utils.class_weight import compute_class_weight"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download and unzip data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def download_url(url, save_path, chunk_size=128):\n",
    "    r = requests.get(url, stream=True)\n",
    "    with open(save_path, 'wb') as fd:\n",
    "        for chunk in r.iter_content(chunk_size=chunk_size):\n",
    "            fd.write(chunk)\n",
    "            \n",
    "PATH_data=\"http://emodb.bilderbar.info/download/download.zip\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "download_url(PATH_data, \"./download.zip\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "with zipfile.ZipFile(\"./download.zip\", 'r') as zip_ref:\n",
    "    zip_ref.extractall(\"./emo-db/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## prepare labels from the dataset\n",
    "\n",
    "we will get labels for two classification problems: \n",
    "\n",
    "1. high vs. low arousal emotions\n",
    "2. positive vs. negative emotions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH_AUDIO=os.path.abspath(\"./emo-db/wav\")+\"/\"\n",
    "labelsd='WLEAFTN'\n",
    "labelshl=  [0, 1, 0, 0, 0, 1, 1] # 0 high arousal emotion, 1 low arousal emotions\n",
    "labelspn=  [0, 0, 0, 0, 1, 0, 1] # 0 negative valence emotion, 1 positive valence emotion\n",
    "\n",
    "hf=os.listdir(PATH_AUDIO)\n",
    "hf.sort()\n",
    "\n",
    "yArousal=np.zeros(len(hf))\n",
    "yValence=np.zeros(len(hf))\n",
    "for j in range(len(hf)):\n",
    "    name_file=hf[j]\n",
    "    label=hf[j][5]\n",
    "    poslabel=labelsd.find(label)\n",
    "    yArousal[j]=labelshl[poslabel]\n",
    "    yValence[j]=labelspn[poslabel]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## compute features using disvoice: phonation, articulation, prosody, phonological"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/camilo/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/camilo/anaconda3/lib/python3.6/site-packages/torch/cuda/__init__.py:52: UserWarning: CUDA initialization: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx (Triggered internally at  /pytorch/c10/cuda/CUDAFunctions.cpp:100.)\n",
      "  return torch._C._cuda_getDeviceCount() > 0\n"
     ]
    }
   ],
   "source": [
    "phonationf=Phonation()\n",
    "articulationf=Articulation()\n",
    "prosodyf=Prosody()\n",
    "phonologicalf=Phonological()\n",
    "replearningf=RepLearning('CAE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## phonation features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing 14a07Aa.wav:  69%|██████▉   | 370/535 [00:24<00:12, 13.37it/s]../phonation/phonation.py:161: WavFileWarning: Chunk (non-data) not understood, skipping it.\n",
      "  fs, data_audio=read(audio)\n",
      "Processing 16b10Wb.wav: 100%|██████████| 535/535 [00:33<00:00, 15.77it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([535, 28])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "Xphonation=phonationf.extract_features_path(PATH_AUDIO, static=True, plots=False, fmt=\"torch\")\n",
    "print(Xphonation.size())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## articulation features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing 03a01Fa.wav:   0%|          | 0/535 [00:00<?, ?it/s]/home/camilo/anaconda3/lib/python3.6/site-packages/numpy/core/_asarray.py:136: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  return array(a, dtype, copy=False, order=order, subok=True)\n",
      "Processing 14a07Aa.wav:  69%|██████▉   | 370/535 [05:54<02:51,  1.04s/it]../articulation/articulation.py:264: WavFileWarning: Chunk (non-data) not understood, skipping it.\n",
      "  fs, data_audio=read(audio)\n",
      "Processing 16b10Wb.wav: 100%|██████████| 535/535 [08:29<00:00,  1.05it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([535, 488])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "Xarticulation=articulationf.extract_features_path(PATH_AUDIO, static=True, plots=False, fmt=\"torch\")\n",
    "print(Xarticulation.size())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## prosody features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing 14a07Aa.wav:  69%|██████▉   | 370/535 [02:20<00:57,  2.85it/s]../prosody/prosody.py:300: WavFileWarning: Chunk (non-data) not understood, skipping it.\n",
      "  fs, data_audio=read(audio)\n",
      "Processing 14a07Fd.wav:  70%|██████▉   | 372/535 [02:21<00:58,  2.77it/s]../prosody/prosody.py:300: WavFileWarning: Chunk (non-data) not understood, skipping it.\n",
      "  fs, data_audio=read(audio)\n",
      "Processing 14a07Na.wav:  70%|███████   | 375/535 [02:21<00:53,  3.00it/s]../prosody/prosody.py:300: WavFileWarning: Chunk (non-data) not understood, skipping it.\n",
      "  fs, data_audio=read(audio)\n",
      "Processing 14a07Wc.wav:  70%|███████   | 377/535 [02:22<00:42,  3.69it/s]../prosody/prosody.py:300: WavFileWarning: Chunk (non-data) not understood, skipping it.\n",
      "  fs, data_audio=read(audio)\n",
      "Processing 16b10Wb.wav: 100%|██████████| 535/535 [03:18<00:00,  2.70it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([535, 103])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "Xprosody=prosodyf.extract_features_path(PATH_AUDIO, static=True, plots=False, fmt=\"torch\")\n",
    "print(Xprosody.size())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## phonological features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing 14a07Aa.wav:  69%|██████▉   | 370/535 [02:22<01:10,  2.33it/s]/home/camilo/anaconda3/lib/python3.6/site-packages/phonet/phonet.py:235: WavFileWarning: Chunk (non-data) not understood, skipping it.\n",
      "  fs, signal=read(audio_file)\n",
      "Processing 16b10Wb.wav: 100%|██████████| 535/535 [03:24<00:00,  2.62it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([535, 108])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "Xphonological=phonologicalf.extract_features_path(PATH_AUDIO, static=True, plots=False, fmt=\"torch\")\n",
    "print(Xphonological.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## representation learning features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing 03a01Fa.wav:   0%|          | 0/535 [00:00<?, ?it/s]/home/camilo/anaconda3/lib/python3.6/site-packages/torch/nn/functional.py:1639: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n",
      "  warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n",
      "Processing 08b01Wa.wav:  16%|█▌        | 83/535 [00:33<03:00,  2.50it/s]../replearning/AEspeech.py:95: UserWarning: There is Inf values in the Mel spectrogram\n",
      "  warnings.warn(\"There is Inf values in the Mel spectrogram\")\n",
      "Processing 14a07Aa.wav:  69%|██████▉   | 370/535 [02:41<01:26,  1.91it/s]../replearning/AEspeech.py:77: WavFileWarning: Chunk (non-data) not understood, skipping it.\n",
      "  fs, signal=read(wav_file)\n",
      "Processing 16b10Wb.wav: 100%|██████████| 535/535 [03:53<00:00,  2.29it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([535, 1536])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "Xrep=replearningf.extract_features_path(PATH_AUDIO, static=True, plots=False, fmt=\"torch\")\n",
    "print(Xrep.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## define architecture of a simple neural network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE=16\n",
    "LR=0.001\n",
    "class model1(nn.Module):\n",
    "    def __init__(self, feat_in):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.linear1=nn.Linear(feat_in, 128)\n",
    "        self.linear2=nn.Linear(128,64)\n",
    "        self.linear3=nn.Linear(64,2)\n",
    "        self.dropout=nn.Dropout(p=0.3)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x=self.dropout(F.leaky_relu(self.linear1(x)))\n",
    "        x=self.dropout(F.leaky_relu(self.linear2(x)))\n",
    "        x=F.softmax(self.linear3(x))\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## define dataset loader to train the DNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Tdataset(data.Dataset):\n",
    "    def __init__(self, tensor, labels):\n",
    "        self.tensor=torch.from_numpy(tensor).float()\n",
    "        self.labels=torch.from_numpy(labels).long()\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.labels)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        X=self.tensor[idx].float()\n",
    "        y=self.labels[idx]\n",
    "        return X, y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## methods to train - evaluate the neural network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit(model, train_loader, test_loader, optimizer, criterion, n_epochs=20):\n",
    "\n",
    "    if torch.cuda.is_available():\n",
    "        model=model.cuda()  \n",
    "    valid_loss_min = np.Inf # set initial \"min\" to infinity\n",
    "    train_loss_min=np.Inf\n",
    "    correct_test_max=0\n",
    "    \n",
    "\n",
    "    for epoch in range(n_epochs):\n",
    "        correct_train=0\n",
    "        correct_test=0\n",
    "        start=time.time()\n",
    "        # monitor training loss\n",
    "        train_loss = 0.0\n",
    "        valid_loss = 0.0\n",
    "        model.train() # prep model for training\n",
    "        total_train=0\n",
    "        for X, y in train_loader:\n",
    " \n",
    "            # clear the gradients of all optimized variables\n",
    "            optimizer.zero_grad()\n",
    "            if torch.cuda.is_available():\n",
    "                X=X.cuda()\n",
    "                y=y.cuda()\n",
    "\n",
    "            data_out=model(X)\n",
    "            \n",
    "            if torch.cuda.is_available():\n",
    "                data_out=data_out.cuda()\n",
    "            loss = criterion(data_out, y)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            train_loss += loss.item()*data_out.size(0)\n",
    "            \n",
    "            _, predicted = torch.max(data_out.data, 1)\n",
    "            total_train += y.size(0)\n",
    "            correct_train += (predicted == y).sum().item()\n",
    "            \n",
    "        ######################    \n",
    "        # validate the model #\n",
    "        ######################\n",
    "        model.eval() # prep model for evaluation\n",
    "        total_val=0\n",
    "        for X, y in test_loader:\n",
    "            # forward pass: compute predicted outputs by passing inputs to the model\n",
    "            if torch.cuda.is_available():\n",
    "                X=X.cuda()\n",
    "                y=y.cuda()\n",
    "\n",
    "            data_val_out=model(X)\n",
    "\n",
    "            if torch.cuda.is_available():\n",
    "                data_val_out=data_val_out.cuda()\n",
    "\n",
    "            # calculate the loss\n",
    "            loss = criterion(data_val_out, y)\n",
    "            # update running validation loss \n",
    "            valid_loss += loss.item()*data_val_out.size(0)\n",
    "            _, predicted = torch.max(data_val_out.data, 1)\n",
    "            total_val += y.size(0)\n",
    "            correct_test+= (predicted == y).sum().item()\n",
    "            \n",
    "        train_loss = train_loss/len(train_loader.dataset)\n",
    "        correct_train=correct_train/total_train\n",
    "        valid_loss = valid_loss/len(test_loader.dataset)\n",
    "        correct_test=correct_test/total_val\n",
    "\n",
    "        print('Epoch: {} \\tTraining Loss: {:.6f} \\tValid Loss: {:.6f} \\tACC train {:.6f} \\tACC test {:.6f}'.format(\n",
    "            epoch, \n",
    "            train_loss,\n",
    "            valid_loss,\n",
    "            correct_train, \n",
    "            correct_test,\n",
    "            ))\n",
    "\n",
    "        # save model if validation loss has decreased\n",
    "        if correct_test >= correct_test_max:\n",
    "            print('Validation Accuracy increased ({:.6f} --> {:.6f}).  Saving model ...'.format(\n",
    "                correct_test_max,\n",
    "                correct_test))\n",
    "\n",
    "            modelr=model\n",
    "            correct_test_max=correct_test\n",
    "\n",
    "\n",
    "    return modelr\n",
    "\n",
    "\n",
    "def predict(model, test_loader):\n",
    "\n",
    "    model.eval() # prep model for evaluation\n",
    "    y_pred=[]\n",
    "    for X, y in test_loader:\n",
    "        # forward pass: compute predicted outputs by passing inputs to the model\n",
    "        if torch.cuda.is_available():\n",
    "            X=X.cuda()\n",
    "        data_val_out=model(X)\n",
    "\n",
    "        if torch.cuda.is_available():\n",
    "            data_val_out=data_val_out.cpu()\n",
    "        y_pred.append(data_val_out.detach().numpy())\n",
    "\n",
    "    y_pred=np.concatenate(y_pred, axis=0)\n",
    "\n",
    "    return y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Emotion classification using a DNN trained in pytorch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def classify(X, y):\n",
    "    \n",
    "    # train test split\n",
    "    Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size=0.30, random_state=42)\n",
    "    \n",
    "    # z-score standarization\n",
    "    scaler = preprocessing.StandardScaler().fit(Xtrain) \n",
    "    Xtrain=scaler.transform(Xtrain) \n",
    "    Xtest=scaler.transform(Xtest)\n",
    "    \n",
    "\n",
    "    weights=compute_class_weight('balanced', np.unique(ytrain), ytrain)\n",
    "    class_weights=torch.from_numpy(weights).float()\n",
    "    train=Tdataset(Xtrain, ytrain)\n",
    "    test=Tdataset(Xtest, ytest)\n",
    "\n",
    "    train_loader = torch.utils.data.DataLoader(train, batch_size=BATCH_SIZE, drop_last=True, num_workers=0)\n",
    "    test_loader = torch.utils.data.DataLoader(test, batch_size=1, drop_last=True, num_workers=0)\n",
    "\n",
    "    model=model1(X.size()[1])\n",
    "    criterion = torch.nn.CrossEntropyLoss(weight=class_weights)\n",
    "    optimizer = torch.optim.Adam(model.parameters(), lr = LR)\n",
    "        \n",
    "    model=fit(model, train_loader, test_loader, optimizer, criterion)\n",
    "    \n",
    "    ypred=predict(model, test_loader)\n",
    "    score_test=ypred[:,1]\n",
    "    ypred=np.argmax(ypred, 1)\n",
    "\n",
    "    acc=metrics.accuracy_score(ytest, ypred)\n",
    "    dfclass=metrics.classification_report(ytest, ypred,digits=4)\n",
    "\n",
    "    # display the results\n",
    "    \n",
    "    plot_confusion_matrix(ytest, ypred, classes=[\"class 0\", \"class 1\"], normalize=True)\n",
    "    plot_ROC(ytest, score_test)\n",
    "    plot_histogram(ytest, score_test, name_clases=[\"class 0\", \"class 1\"])\n",
    "    \n",
    "    print(\"Accuracy: \", acc)\n",
    "    print(dfclass)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## classify high vs. low arousal with the different feature sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/camilo/.local/lib/python3.6/site-packages/ipykernel_launcher.py:15: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 \tTraining Loss: 0.619256 \tValid Loss: 0.558432 \tACC train 0.733696 \tACC test 0.819876\n",
      "Validation Accuracy increased (0.000000 --> 0.819876).  Saving model ...\n",
      "Epoch: 1 \tTraining Loss: 0.486358 \tValid Loss: 0.472781 \tACC train 0.877717 \tACC test 0.826087\n",
      "Validation Accuracy increased (0.819876 --> 0.826087).  Saving model ...\n",
      "Epoch: 2 \tTraining Loss: 0.433817 \tValid Loss: 0.453279 \tACC train 0.877717 \tACC test 0.850932\n",
      "Validation Accuracy increased (0.826087 --> 0.850932).  Saving model ...\n",
      "Epoch: 3 \tTraining Loss: 0.410452 \tValid Loss: 0.441338 \tACC train 0.885870 \tACC test 0.875776\n",
      "Validation Accuracy increased (0.850932 --> 0.875776).  Saving model ...\n",
      "Epoch: 4 \tTraining Loss: 0.396105 \tValid Loss: 0.438108 \tACC train 0.910326 \tACC test 0.863354\n",
      "Epoch: 5 \tTraining Loss: 0.383713 \tValid Loss: 0.436888 \tACC train 0.921196 \tACC test 0.863354\n",
      "Epoch: 6 \tTraining Loss: 0.380010 \tValid Loss: 0.440280 \tACC train 0.937500 \tACC test 0.863354\n",
      "Epoch: 7 \tTraining Loss: 0.375114 \tValid Loss: 0.441050 \tACC train 0.929348 \tACC test 0.863354\n",
      "Epoch: 8 \tTraining Loss: 0.365994 \tValid Loss: 0.445213 \tACC train 0.942935 \tACC test 0.850932\n",
      "Epoch: 9 \tTraining Loss: 0.364372 \tValid Loss: 0.446567 \tACC train 0.945652 \tACC test 0.844720\n",
      "Epoch: 10 \tTraining Loss: 0.355944 \tValid Loss: 0.449147 \tACC train 0.951087 \tACC test 0.844720\n",
      "Epoch: 11 \tTraining Loss: 0.360320 \tValid Loss: 0.451907 \tACC train 0.948370 \tACC test 0.844720\n",
      "Epoch: 12 \tTraining Loss: 0.355045 \tValid Loss: 0.450470 \tACC train 0.956522 \tACC test 0.863354\n",
      "Epoch: 13 \tTraining Loss: 0.347873 \tValid Loss: 0.452698 \tACC train 0.961957 \tACC test 0.857143\n",
      "Epoch: 14 \tTraining Loss: 0.347859 \tValid Loss: 0.456846 \tACC train 0.961957 \tACC test 0.850932\n",
      "Epoch: 15 \tTraining Loss: 0.345346 \tValid Loss: 0.454722 \tACC train 0.964674 \tACC test 0.850932\n",
      "Epoch: 16 \tTraining Loss: 0.341563 \tValid Loss: 0.453203 \tACC train 0.970109 \tACC test 0.850932\n",
      "Epoch: 17 \tTraining Loss: 0.343015 \tValid Loss: 0.455295 \tACC train 0.964674 \tACC test 0.850932\n",
      "Epoch: 18 \tTraining Loss: 0.335364 \tValid Loss: 0.452552 \tACC train 0.975543 \tACC test 0.850932\n",
      "Epoch: 19 \tTraining Loss: 0.335353 \tValid Loss: 0.455069 \tACC train 0.975543 \tACC test 0.850932\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.8509316770186336\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9011    0.8454    0.8723        97\n",
      "         1.0     0.7857    0.8594    0.8209        64\n",
      "\n",
      "    accuracy                         0.8509       161\n",
      "   macro avg     0.8434    0.8524    0.8466       161\n",
      "weighted avg     0.8552    0.8509    0.8519       161\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classify(Xphonation, yArousal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 \tTraining Loss: 0.562844 \tValid Loss: 0.457760 \tACC train 0.793478 \tACC test 0.913043\n",
      "Validation Accuracy increased (0.000000 --> 0.913043).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/camilo/.local/lib/python3.6/site-packages/ipykernel_launcher.py:15: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 \tTraining Loss: 0.389061 \tValid Loss: 0.381554 \tACC train 0.934783 \tACC test 0.944099\n",
      "Validation Accuracy increased (0.913043 --> 0.944099).  Saving model ...\n",
      "Epoch: 2 \tTraining Loss: 0.344064 \tValid Loss: 0.373976 \tACC train 0.975543 \tACC test 0.944099\n",
      "Validation Accuracy increased (0.944099 --> 0.944099).  Saving model ...\n",
      "Epoch: 3 \tTraining Loss: 0.329644 \tValid Loss: 0.359735 \tACC train 0.980978 \tACC test 0.956522\n",
      "Validation Accuracy increased (0.944099 --> 0.956522).  Saving model ...\n",
      "Epoch: 4 \tTraining Loss: 0.323040 \tValid Loss: 0.358172 \tACC train 0.986413 \tACC test 0.956522\n",
      "Validation Accuracy increased (0.956522 --> 0.956522).  Saving model ...\n",
      "Epoch: 5 \tTraining Loss: 0.319871 \tValid Loss: 0.358411 \tACC train 0.986413 \tACC test 0.956522\n",
      "Validation Accuracy increased (0.956522 --> 0.956522).  Saving model ...\n",
      "Epoch: 6 \tTraining Loss: 0.313152 \tValid Loss: 0.354601 \tACC train 0.997283 \tACC test 0.956522\n",
      "Validation Accuracy increased (0.956522 --> 0.956522).  Saving model ...\n",
      "Epoch: 7 \tTraining Loss: 0.311442 \tValid Loss: 0.354859 \tACC train 0.997283 \tACC test 0.956522\n",
      "Validation Accuracy increased (0.956522 --> 0.956522).  Saving model ...\n",
      "Epoch: 8 \tTraining Loss: 0.311784 \tValid Loss: 0.356205 \tACC train 0.997283 \tACC test 0.956522\n",
      "Validation Accuracy increased (0.956522 --> 0.956522).  Saving model ...\n",
      "Epoch: 9 \tTraining Loss: 0.311317 \tValid Loss: 0.355951 \tACC train 0.997283 \tACC test 0.950311\n",
      "Epoch: 10 \tTraining Loss: 0.309646 \tValid Loss: 0.354990 \tACC train 1.000000 \tACC test 0.956522\n",
      "Validation Accuracy increased (0.956522 --> 0.956522).  Saving model ...\n",
      "Epoch: 11 \tTraining Loss: 0.308828 \tValid Loss: 0.356467 \tACC train 1.000000 \tACC test 0.956522\n",
      "Validation Accuracy increased (0.956522 --> 0.956522).  Saving model ...\n",
      "Epoch: 12 \tTraining Loss: 0.308480 \tValid Loss: 0.356755 \tACC train 1.000000 \tACC test 0.956522\n",
      "Validation Accuracy increased (0.956522 --> 0.956522).  Saving model ...\n",
      "Epoch: 13 \tTraining Loss: 0.308608 \tValid Loss: 0.356378 \tACC train 1.000000 \tACC test 0.956522\n",
      "Validation Accuracy increased (0.956522 --> 0.956522).  Saving model ...\n",
      "Epoch: 14 \tTraining Loss: 0.308536 \tValid Loss: 0.354919 \tACC train 1.000000 \tACC test 0.950311\n",
      "Epoch: 15 \tTraining Loss: 0.308547 \tValid Loss: 0.354924 \tACC train 1.000000 \tACC test 0.950311\n",
      "Epoch: 16 \tTraining Loss: 0.308424 \tValid Loss: 0.354529 \tACC train 1.000000 \tACC test 0.950311\n",
      "Epoch: 17 \tTraining Loss: 0.308350 \tValid Loss: 0.354284 \tACC train 1.000000 \tACC test 0.950311\n",
      "Epoch: 18 \tTraining Loss: 0.308434 \tValid Loss: 0.355305 \tACC train 1.000000 \tACC test 0.950311\n",
      "Epoch: 19 \tTraining Loss: 0.308468 \tValid Loss: 0.356107 \tACC train 1.000000 \tACC test 0.950311\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVgAAAFRCAYAAAAxY7WIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAHfNJREFUeJzt3Xl8VfWZx/HPE5ZAICSQgLQiKC2Wza0FqUrVV11QlLbOuFSrdRmkiFjrMh2V0bZKW7WKdalS6LiMK6O+HNuK0o5VKKAooiMKboBo6wYxxGASlfDMH+eEuaYkudzcc+/9Jd83r/vKPffknvvgMV+e/H5nMXdHRESyryjfBYiIdFQKWBGRhChgRUQSooAVEUmIAlZEJCEKWBGRhChgRUQSooAVEUmIAlZEJCFd812AiMiO6tJniPuW+ozf7/UbFrj7EVksabsUsCISHN9ST/FXjs/4/Q0v/KYyi+W0SAErIgEysMIf4VTAikh4DDDLdxVtKvx/AkREAqUOVkTCpCECEZGEBDBEoIAVkQBpkktEJDnqYEVEEmAE0cEWfoUiIoFSBysiATINEYiIJCaAIQIFrIiESR2siEgSwjhMq/ArFBEJlDpYEQlPIBd7UcCKSJgCGCJQwIpIgMIYg1XAikiYijREICKSfTpVVkSkc1MHKyJh0lEEIiJJ0CSXiEhy1MGKiCQkgA628CsUEQmUOlgRCY/perAiIskJYIhAASsiYVIHKyKSBB2mJSKSnAA62ML/J0BEJFDqYEUkPIFc7EUBKyIB0hisiEhyAhiDVcCKSJgC6GALv0IRkUCpgxWRMGmIQEQkAaZJrryzrj3dupfmuwzJkr1HDM53CZJlz694bqO798/ozepg88u6l1I8/IR8lyFZsuTpG/JdgmRZSfei9Zm+1xSwIiLZZ4QRsIU/iCEiEih1sCISHosfBU4BKyIBsiCGCBSwIhIkBayISEIUsCIiCQkhYHUUgYhIQtTBikh4Ej6KwMwuAD4Aytz9ppTXjwEq4sU6d7+nte2ogxWR4Fh8FEGmj1a3bTYeqHD3O4G+ZjYuZfW57v47d/8dcEZbdSpgRSRISQUsMBFYHT9fFS83ec7MLjezMcDNbW1IASsiQWpnwFaa2fKUx5SUTVcC1fHzBmBgyrpLgS8BvwIWtVWjxmBFpDPa6O5jWli3ASiJn5cCVSnrrgamAgcC9wGHtvYh6mBFJEgJDhHMB/aMn48EFphZWbw8yt1r3f0RoFtbG1LAikh4rJ2PVrj7EqDBzM4ANsWP2fHqa83sHDP7J+C3bZWpIQIRCVKSJxq4+8xmL50Yv/7HHdmOAlZEgmO62IuISHJCCFiNwYqIJEQdrIiEqfAbWAWsiATIwhgiUMCKSJAUsCIiCQkhYDXJJSKSEHWwIhIcHQcrIpKkws9XBayIBEhHEYiIJEcBKyKSkBACVkcRiIgkRB2siISp8BtYBayIhCmEIQIFrIgEJ81bv+SdAlZEghRCwGqSS0QkIepgRSRIIXSwClgRCVPh56sCVkTCpA5WRCQJuhaBiEgyDAggX3UUgYhIUtTBikiAdKKBiEhiAshXBayIhEkdrIhIEkwdrIhIIgwoKir8hNVRBCIiCVEHKyJB0hCBiEhCNMklIpIETXJJNp178jf5oLqWst49mT1v0bbXDxq7O0MHVTJsSH/u+sMzrFrzLgDzrpnMvnvuxvxFL3H2zHvzVba04PrrrqV//wHUfFTDWdOmb3v99dde44H751FSUsLEoyYxbPfdATjh2H9i2bKnmHjU0dw8e26+yi4Y0amyhZ+wmuQKwP57D6VfeS/ufeRZykt7Mnb0kG3rLpo8gdseWsqs2x/nun87DoCvjRzM3AcXs9vhMxSuBWjpksVUVVVx0smnsKm6mmeeWbZt3YUX/Ihzzj2PqdOmc+mMiwFYvvxZJk/5AW++/a7CNTAK2ABMGD+KV9a9B8Ara99jwvhRAAzoV0r/vr0B2LhpM0MHVQJw0Nhh3HLpScz92cn07NEtP0VLix57dD7Dh48AYMSIkSx4dD4A9fX1rFuzht69e1NcXMybb65jy5YtLHryCaZNPZMzzziNurq6fJZeQGzbfbkyeeRKTgLWzHqb2V1mdlAuPq+jqSjvxaaPoh+shk+3sFNFKQBVNR/Tr6wXg3Yqp2vXIj7b0gjArDseZ/ikn1K16WMuPO2wvNUt21dVtZHyvn0BKO7Rg/ffj/7xrK6uprRPn23f17VrVzZs2MD5F/6Y1a+tpaKygmuuvjIvNRcis8wfuZKTgHX3zcA6snANcjO7wMxOMbPpbX93x7CxejMlPboDUFpSzIc1Udg2Nm7ltBl3cP6ph/Kjkw9hyfNrtr2nsXErM254mF13rshLzdKyysr+1Med6ObaWvr1i/ZRRUUFnzQ0bPu++ro6ysvLgShsZ/7iKta/+WbO6y1U6mA/r7G9GzCz8UCFu98J9DWzce0vq/AtWPwyo4d9EYDhQwfy56Wr6dO7BwCLlr/O+Vc/wM47lfOTm/7wufeVlhSz9IW1Oa9XWnfEkRNZufJFAFavXsVhh0+gpqaG4uJidhkyhLq6OhoaGth5l13o2bMn7g5AbW0t+x1wQD5LLxzt6F5z2cEmchSBmXUHpgDdgBHuPiVl3S7AZcBKoNHdf2NmZwGbgW+5+3HNl1M2PRFYHT9fFS8vS1mPmU2JPxu69U7gb5d7T/3vOg4cszvf//bXqamtp6a2nhsv+S6nXnI7Y0YNYfjQgdxw9xO8s6EGgL/ceh7LX17Py2+8w20PLc1z9dLcfvsfwMInn+CO22+lvLycsrJyfnj2Wdxx1z3M/PmVzLrmaoqLi7nq6msBOOTgbzBm7FhGjhrN6WdMznP1hSGUowis6V/HrG7UbBqwwt2fNrNz3f16M/sp8CRQCwwElgP3uPshZrYAmAz0dfcXmy+nbHcO8Ht3/6OZHUUUwD9oqY6ikgFePPyErP/9JD8+XHZDvkuQLCvpXvScu4/Z0ff12vkrPnzq7Iw/d8Vl38zoc3dUUkMEewB1AO5+fbN1K4FhwCigS/zajcBC4OgWlptsAEri56VAVVarFpFghDBEkFTAvg6cCmBmh5pZccq6yUTBujDltfeAvYDvmVnFdpabzAf2jJ+PBB5LpnwRKXSdeZJrLrCnma0AKomGTEYD+wGvAlOBaUCpmY0CbgGOAR5296rtLAPg7kuABjM7A9jk7osQkU4phA42kUkud68Fmh+AeWzK82Hx1xvjr2Obvf9zy83WzWx3gSIStkBu260zuUREEqKLvYhIcKLDtPJdRdsUsCISIN22W0QkMQHkqwJWRMKkDlZEJAmB3NFARxGIiCREHayIBCeUi70oYEUkSApYEZGEBJCvClgRCVMIHawmuUREEqIOVkTCE8hhWgpYEQmO6VRZEZHkBJCvClgRCVNRgglrZhcAHwBl7n5Ts3XDgW8AL7n7U63WmFiFIiIJSuqOBmY2Hqhw9zuBvmY2LmXdV4Az3X1uW+EKClgRkeYmAqvj56vi5SY3AOvN7Po4iFulIQIRCY61/5YxlWa2PGV5jrvPaVoHVMfPG4CB0WdaL2BX4CZgELDMzIa4+6ctfYgCVkSCVNS+IdiN7j6mhXUbgJL4eSnQdOPV7kC9u28F3jKzd4jC960Wa2xXiSIieZLgbbvnA3vGz0cCC8yszN2rgU/MrHe8bgPw99Y2pIAVkSAlNcnl7kuABjM7A9gUP2bHq6cDF5vZd4Gr3L2xtW1piEBEpBl3n9nspRPj158Fnk13OwpYEQmOEZ3NVegUsCISpHZOcuWEAlZEwpPeZFXeKWBFJEgB5KsCVkTCYyR7LYJs0WFaIiIJUQcrIkEKoIFVwIpImDTJJSKSgHTOyCoEClgRCVIIk1wKWBEJUuHHa5pHEZjZ5WY22syOMLNnzeyspAsTEQlduh3sB8Aa4GGiq3vvm1hFIiJpCGGSK93jYHsBdwL/AbxPfGUZEZF8iE40yPyRK+l2sLOAge7+tpntClycWEUiIm0J5FoE6XawlwJlZnYE8ACwf3IliYi0LakLbmeTxmBFRBKiMVgRCVKC9+TKmkzHYGckVpGISBuaJrkKXboBuw9wmJl1Ifq77QsclVhVIiJtCGGSK92APR54BqgAXgXqE6tIRCQNhR+v6Y/BNgBPAyVANTApsYpERNpgFl2LINNHrqTbwS4CDgTuBa4DHk2sIhGRDiKtgHX3P6UsnmBm4xKqR0QkLQEMwbYcsGb2PPAR0Nh8FTAIGJZgXSIirQp9kus77r5+eyvMbEBC9YiIpCWAfG05YFPD1cxOBT5x9/vMbCLwNtHZXSIiOWfkdrIqU+keRTAW+BOAu88nmugSEZFWpHsUwUpgE4CZHQ18IbGKRETa0sHuyfUoMNfMvgJsBI5LrqTs2WfEYJYsuzHfZUiW9B07Pd8lSAEJfZJrG3d/C/iXhGsREUlbuuOb+aSbHopIcIwO1ME2Z2ZF7r4128WIiKQr6KtpmdnJtNyFjwF+mEhFIiIdRGsd7D7Ay/zjmVwAnkw5IiLpCbqDBX7s7tsLV8zsjYTqERFpU3RvrcJP2NbO5NoWrmZ2BXAo8AnRsIEB30i8OhGRFoTQwaZ7pMMbRAH7K3c/EHgkuZJERNrWke4qeyCwFehlZjcQ3S7mysSqEhHpANIN2HOBcnf/m5kdCdySYE0iIq2KbnpY+GME6Q4RnAycHz/vQnQbbxGRvClqxyNX0u1gRwBPALj7H81sOdGxsCIieRFAA5t2wC4D6sysiGi4oGdyJYmItM5yfPPCTKXbLT9DdOvu5cDBBHI1LRHpuDrMUQTu/gYwuWnZzA4GViVUk4hIh9BqB2tmXcxskpmNSHltZ2BW4pWJiLSiyDJ/5EpbHeytwFeB8vhOBnsB/w5clHRhIiItCeUwrbYCtsrd9zCzMuCvRGd0jXd33fBQRPIqgHxtc5Lr72bWDagH5hCNw26J7zIrIpIf7RgeKKQhgsuBs4k6cohONjCgArgjwbpERFplFH4L21bAHu7uS5q/aGb7JlSPiEiH0WrAbi9c49efSaYcEZG2RZNc+a6ibbrpoYgESQErIpKQoO9oICJSqEIZIsjllbtERDoVdbAiEp4cX7QlUwpYEQlSRzhVVkSk4IQyBquAFZEgBdDAKmBFJERGUQCnyuooAhGRZszsAjM7xcymt7D+P+IbD7RKASsiwTGSu2WMmY0HKtz9TqCvmY1rtn4S0DudOhWwIhKeZC9XOBFYHT9fFS9HH2u2G9HQ6urtvO8fKGBFJEhF8Z1lM3kAlWa2POUxJWXTlUB1/LwBGAhgZl2BI939oXRr1CSXiASnaYigHTa6+5gW1m0ASuLnpUBV/PxA4GQzOx7YFfiOmR3l7n9v6UPUwYqIfN58YM/4+UhggZmVuftf3H1/dz8YuB34UWvhCgpYEQlUO4cIWhRfB7vBzM4ANsWP2ZnUqCECEQlSkicauPvMZi+d2Gz9T9PZjgJWRIJjhPHrtwJWRMJjuuC2iEhiCj9ew+iyRUSCpA5WRIITXa6w8HtYBayIBKnw41UBKyKBCqCBVcCKSIgsiKMINMklIpIQdbAiEhydaCAikqAQhggUsCISpMKPVwVsMH593bUM6D+Ampoazjr7/28T9Pprr/HA/fPoWVLCUUdNYtjuuwNw/LHH8MzTTzHxqEnc/Nu5+SpbWnDuKd/kgw9rKevdk9nzFm17/aCxuzN0UCXDhgzgrj8sY9WadwGYd+2Z7LvnbsxftJKzr7g3X2UXjkBOlQ1hGKPTW7J4MR9WVXHSyaewaVM1zyxbtm3dheefyznnnsdZ06bz7zMuAmD5s89y5pSpvPm39xSuBWj/vYfSr6wX9z7yLOV9Shg7esi2dRdNnsBtDy1l1h3/w3UXHQ/A10YOZu4Di9ntsEsUrrGmMdhMH7migA3AgsfmM3z4CACGjxjJgsfmA1BfX8/atWvo3bs3xcXFrF+3ji1btrBw4ROc9YPJTD79VOrq6vJZumzHhPGjeGXdewC8svY9JowfBcCAfqX071cKwMbqzQwdVAlEXe0tl53E3MtPoWePbvkpWjKSk4A1s95mdpeZHZSLz+toqjZupLxvXwB69OjB++9FP5zV1dX0Ke2z7fu6dO3Khg0buODCH/PK6+uoqKzkmquvzEvN0rKK8t5s+qgegIZPPmOnimgfVtV8TL+yXgzaqZyuXYv4bEsjALPu+B+GH/0TqjZt5sLTD89b3YXGzDJ+5EpOAtbdNwPryMK4tJkdaGaPt7+qcFT277+tE62traVfRQUAFRUVNHzSsO376uvrKC8vB6Br1678/JdX8ea6dbkvWFq1sXozJXEnWtqrBx/WfAxAY+NWTrvkds4/7TB+dMohLHl+zbb3NDZuZcb1D7PrFyvyUnMhsnY8ciWXQwSN2diIuy8CemZjW6GYcMREXlr5IgCvrF7FYYdPoKamhuLiYgYPHkJdXR0NDQ0MGrQLPXv2xN2BKIz3P2B8PkuX7Viw+GVGD9sZgOFDB/Lnp1bTp3cPABYtf53zr7qfnQeU85Mbf/+595WWFLP0hTX/sL3OyizzR64kchSBmXUHpgDdgBHuPiVl3S7AZcBKoNHdf2NmZwGbgW+5+3HNl7fzEZ8mUXeh2v+AA1i08AnuuO1WysrKKSsr55yzp/Kfd93LzF9cxaxrrqa4uJirfjULgG8eNJ4xY/dl1KjRnP4vk/NcvTT31P+u5cCxw/j+t79OTW09NbX13Djju5x68e2MGTWE4UMHcsPdT/DOhhoA/nLbeSx/aT0vr3mX2x5amufqC0M0yVX4RxFYU7eT1Y2aTQNWuPvTZnauu19vZj8FngRqie4zvhy4x90PMbMFwGSgr7u/2Hx5O9t/Mr6z4/Y+ewpRuLPL4MFfe23N+qz//SQ/+o6d3vY3SVAaXvjNc63cPrtFw0bt5dfN+1PGnztpj4EZfe6OSmqIYA+gDsDdr2+2biUwDBgFdIlfuxFYCBzdwnLa3H2Ou49x9zH9K/tnULqISHYkFbCvA6cCmNmhZlacsm4yUbAuTHntPWAv4HtmVrGdZRGRFNauP7mSVMDOBfY0sxVAJdGQyWhgP+BVYCowDSg1s1HALcAxwMPuXrWd5W3MbA/gS2Y2OqHaRSQAnXaSy91rgcOavXxsyvNh8dcb469jm73/c8vN1q0EdmlvjSISrlAmuXQtAhEJT4470UzpVFkRkYSogxWRIIXQwSpgRSRIuTwaIFMKWBEJjgFFhZ+vClgRCZM6WBGRhIQwBqujCEREEqIOVkSCpCECEZEEaJJLRCQxub1oS6YUsCISHp0qKyLSuamDFZEgBdDAKmBFJDzRJFfhR6wCVkSCVPjxqoAVkVAFkLAKWBEJUgiHaekoAhGRhKiDFZEgBTDHpYAVkTAFkK8KWBEJVAAJq4AVkeAYmuQSEenU1MGKSHgCudiLAlZEghRAvipgRSRQASSsAlZEAqQLbouIJCaEMVgdRSAikhB1sCISHCOIIVgFrIgEKoCEVcCKSJA0ySUikpAQJrkUsCISpADyVUcRiIgkRR2siIQnkMMI1MGKSJCsHX/a3LbZBWZ2iplNb/b6iWa2zMxWm9mYtrajgBWR4BjRJFemj1a3bTYeqHD3O4G+ZjYuft2AOncfB1wD/KytOhWwIhIka8ejDROB1fHzVfEyHnk4fv1Z4N22NqQxWBHpjCrNbHnK8hx3n9O0DqiOnzcAA7fz/kOBWW19iAJWRMLUvkmuje7e0hjqBqAkfl4KVH3uY82+DKx391VtfYiGCEQkSAlOcs0H9oyfjwQWmFkZgJntBOzl7g+aWW8z69XahhSwIhKkpCa53H0J0GBmZwCb4sdsM6sAFgAXx8MLC4G61ralIQIRCVKSh8G6+8xmL50Yf917R7ajgBWRMOlEAxGRzksdrIgEJzqetfBbWAWsiIQnjcmqQqCAFZEgBZCvClgRCVQACatJLhGRhKiDFZEApXfZwXzr0AG7YsVzG3t2s/X5riMHKoGN+S5CsqYz7c8hmb5Rk1x55u79811DLpjZ8lYuXCGB0f5sWyA3NOjYASsiHVgACauAFZEghTAGq6MIOoY5bX+LBET7s4NQB9sBpFyJXToA7c/0aJJLRCQhAeSrAlZEAqRrEUg+mJm5u+e7DsmO1P2pfdtc4SesArYDafbD+B3gA+Bdd1+X38okUyn7s4u7N5pZkbtvzXddkh4FbAeS8sN4AvARsB/Qx8z+5u5z81qcZMzMLgYOMLNj3P0zhWx8okHhN7A6TKujMbMuRHfCXA/8GvgrsI+ZndjqG6UgmZkBc4Gngf80s27uvtXMOv3PrrXjkSudfid1JPEQQSPwEnAEUE5058s/AzubWfd81ic7ziMb45vwreLzIRtAD5ecpO4qm00K2A4kZQJkLTACOJooZB8GKoCBeSpN2qGpW3X3K4hCdo6ZfaGzT3hZO/7kigK2A2j+66K7PwfcB4wGzgL+GXgdqM19dbKjtrM/tzYL2U+B+8ysZz7qKxgBjBFokitw8bDAVjMbAOzh7o8DuPvjZvYaUQc7DnjY3avzWau0rZX9ubXpSALgUqC7u9fntVhpkwI2cO7ucSdzDFBkZsvcfXO87m3gbWBlPmuU9LWxPxvjrx/ks8ZCEcIAtIYIAtXs18ivEnWqLwOXmNlpeSlKMqb9uWPaM8GlSS5pVcqvkf3NbDrRITyVwJlEHesZZrZPXouUtGl/ZiaESS4NEQQo/jWyB3AwUAUUu/u/mlkfovHWW4iOg5UAaH9mKIAxAnWwAWl23OMg4EiiGeW7zOwmoBH4HrDM3T/MQ4myA7Q/2yeAgwgUsIWuaWyu6ToDZtbLzCYB7wIPALsDvwWKgf3c/TR3X5u/iqU12p+diwK2gJnZXsC3zaxv/MPYD/ghMBR4EHjR3X8ZLy8Fns9ftdIW7c/s0iSXtNcI4DBgfzMrB3YDtgJ3A4uBm81sNNAbWOruVXmrVNKh/Zk17Zni0iSXAO5+n5m9RXRdgSLgVeAx4ETgJuD7wFXAsTrovPBpf2aPrqYl7RJfFQt3X0o0Nrcv0fjcq0A10TjdTsCZ+mEsfNqfnZMCtgA1XRXLzPqZ2UxgA/DfRIfsjAMagAXAne7+Th5LlTRof3ZeGiIoQE2zy8AJwBvA3u7+qJl1A6YSHRP5S3dvyGedkh7tz2SEMESggC1cXYGP3P1uADP7MtE1BeYBq/TDGBztzyzL5WRVphSwhasRGGpmY4l+EL8KfOzuj+S3LMmQ9mc26a6y0h7uvtnMHgSmAbsCXwaOymtRkjHtz+zK9RlZmVLAFjB3X2VmFwEDgE/d/W/5rkkyp/2ZZQEkrAK2wMXXAt2c7zokO7Q/OxcFrIgESZNcIiIJ0SSXiEhCAshXBayIBCqAhNWpspJzZnaEmd0fP59uZse0Y1tfMrMX0vi+H5vZJZl+jkgmFLCyw8xsvJl9bGaXmdl/mdnPdnATTwL94+c3u/tDLXxOn/iyfi1y9zVE5/K35Tmg+44UKYVNlyuUDsndF5vZBuBqwIEPzex+d38pzfc3NN0txd23tvKtlwO/Bja1scl0ArYxndokDM+veG5BSXerbMcmNmatmFYoYKW9egKfAbVmdjewFpgE7Ed0ndMyYDQwmegSfeOAPgBmVgrMBua4+0Izm0p0T6pvAWcDY4Bjzex3wIT4s74Tb6uI6K6rVcDw5kWZ2feJLmZ9EvDdlNeLgDnAQqJbskwzs28DveLtHRl/1rZlXSeg8Lj7EfmuIR0aIpD2mAScAxzn7uuJwvUF4GtEIdgFWE0UmpXAlcCNwCwAd68F1hFd0W8isMndbwXmEwXnG0TXTh0MjAfei7c/ErgCeMDdZwNvpRZlZqOAL7j7XcAdQEnK6p5xTfOAr8evTSS6PfY0ok63+bJIRtTBSnv8wd3vT1luBKria5+OBO5x978Cj5nZAKCvuztQl3JD1S3x1z2Igg93nwOQ8j0jgA/c/bF4W0XAzcC/xevrmtU1GqiPtzUv3tbwePljM3ufqBP+JP7+WcBdRP9AnLad5c929D+MCKiDleS8AZxvZl3M7ECiMPuimVXE65v/v/c60S1TMLOvmtkXiMZ3Ld7W6WZWaWY7EQ0dvAPs3/Tmpru1pmzrRDPrZmaDzWyPlO8bBZzg7v8FbIlvnV1KNHxRTzRE0HxZJCMKWNlhZjaO6Ff+f055rYSoCz00Dq3fEv06/gbwZXevIeo4f29m04A+ZjYEGAWMBR4GPjOzl4FvuPu7RMMBlxMNATwIvATMcPdngIuAK8xsBtANOKipFndfATwFvAxMdfeVRKG8B9F1AEaa2S+IAvQY4DxgOtEQxOLtLItkxKLf2EREJNvUwYqIJEQBKyKSEAWsiEhCFLAiIglRwIqIJEQBKyKSEAWsiEhCFLAiIgn5P368BD1i6SyNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.9503105590062112\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9684    0.9485    0.9583        97\n",
      "         1.0     0.9242    0.9531    0.9385        64\n",
      "\n",
      "    accuracy                         0.9503       161\n",
      "   macro avg     0.9463    0.9508    0.9484       161\n",
      "weighted avg     0.9509    0.9503    0.9504       161\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classify(Xarticulation, yArousal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 \tTraining Loss: 0.617481 \tValid Loss: 0.549946 \tACC train 0.714674 \tACC test 0.838509\n",
      "Validation Accuracy increased (0.000000 --> 0.838509).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/camilo/.local/lib/python3.6/site-packages/ipykernel_launcher.py:15: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 \tTraining Loss: 0.472385 \tValid Loss: 0.469780 \tACC train 0.847826 \tACC test 0.844720\n",
      "Validation Accuracy increased (0.838509 --> 0.844720).  Saving model ...\n",
      "Epoch: 2 \tTraining Loss: 0.413470 \tValid Loss: 0.445087 \tACC train 0.902174 \tACC test 0.869565\n",
      "Validation Accuracy increased (0.844720 --> 0.869565).  Saving model ...\n",
      "Epoch: 3 \tTraining Loss: 0.381649 \tValid Loss: 0.433170 \tACC train 0.926630 \tACC test 0.875776\n",
      "Validation Accuracy increased (0.869565 --> 0.875776).  Saving model ...\n",
      "Epoch: 4 \tTraining Loss: 0.359166 \tValid Loss: 0.423880 \tACC train 0.953804 \tACC test 0.875776\n",
      "Validation Accuracy increased (0.875776 --> 0.875776).  Saving model ...\n",
      "Epoch: 5 \tTraining Loss: 0.348515 \tValid Loss: 0.425460 \tACC train 0.970109 \tACC test 0.875776\n",
      "Validation Accuracy increased (0.875776 --> 0.875776).  Saving model ...\n",
      "Epoch: 6 \tTraining Loss: 0.338467 \tValid Loss: 0.420200 \tACC train 0.972826 \tACC test 0.888199\n",
      "Validation Accuracy increased (0.875776 --> 0.888199).  Saving model ...\n",
      "Epoch: 7 \tTraining Loss: 0.333836 \tValid Loss: 0.424532 \tACC train 0.978261 \tACC test 0.881988\n",
      "Epoch: 8 \tTraining Loss: 0.330134 \tValid Loss: 0.434594 \tACC train 0.983696 \tACC test 0.869565\n",
      "Epoch: 9 \tTraining Loss: 0.326212 \tValid Loss: 0.431927 \tACC train 0.986413 \tACC test 0.863354\n",
      "Epoch: 10 \tTraining Loss: 0.324192 \tValid Loss: 0.420531 \tACC train 0.986413 \tACC test 0.894410\n",
      "Validation Accuracy increased (0.888199 --> 0.894410).  Saving model ...\n",
      "Epoch: 11 \tTraining Loss: 0.325891 \tValid Loss: 0.411264 \tACC train 0.983696 \tACC test 0.900621\n",
      "Validation Accuracy increased (0.894410 --> 0.900621).  Saving model ...\n",
      "Epoch: 12 \tTraining Loss: 0.322506 \tValid Loss: 0.407855 \tACC train 0.986413 \tACC test 0.888199\n",
      "Epoch: 13 \tTraining Loss: 0.321115 \tValid Loss: 0.418396 \tACC train 0.989130 \tACC test 0.881988\n",
      "Epoch: 14 \tTraining Loss: 0.320350 \tValid Loss: 0.419002 \tACC train 0.989130 \tACC test 0.894410\n",
      "Epoch: 15 \tTraining Loss: 0.322865 \tValid Loss: 0.413489 \tACC train 0.986413 \tACC test 0.894410\n",
      "Epoch: 16 \tTraining Loss: 0.321418 \tValid Loss: 0.417812 \tACC train 0.989130 \tACC test 0.888199\n",
      "Epoch: 17 \tTraining Loss: 0.317834 \tValid Loss: 0.419592 \tACC train 0.991848 \tACC test 0.888199\n",
      "Epoch: 18 \tTraining Loss: 0.317718 \tValid Loss: 0.420835 \tACC train 0.989130 \tACC test 0.888199\n",
      "Epoch: 19 \tTraining Loss: 0.316650 \tValid Loss: 0.417753 \tACC train 0.991848 \tACC test 0.888199\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.8881987577639752\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8990    0.9175    0.9082        97\n",
      "         1.0     0.8710    0.8438    0.8571        64\n",
      "\n",
      "    accuracy                         0.8882       161\n",
      "   macro avg     0.8850    0.8806    0.8827       161\n",
      "weighted avg     0.8879    0.8882    0.8879       161\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classify(Xprosody, yArousal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 \tTraining Loss: 0.655445 \tValid Loss: 0.629914 \tACC train 0.646739 \tACC test 0.788820\n",
      "Validation Accuracy increased (0.000000 --> 0.788820).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/camilo/.local/lib/python3.6/site-packages/ipykernel_launcher.py:15: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 \tTraining Loss: 0.562179 \tValid Loss: 0.531967 \tACC train 0.796196 \tACC test 0.819876\n",
      "Validation Accuracy increased (0.788820 --> 0.819876).  Saving model ...\n",
      "Epoch: 2 \tTraining Loss: 0.481375 \tValid Loss: 0.472702 \tACC train 0.828804 \tACC test 0.844720\n",
      "Validation Accuracy increased (0.819876 --> 0.844720).  Saving model ...\n",
      "Epoch: 3 \tTraining Loss: 0.435548 \tValid Loss: 0.441714 \tACC train 0.885870 \tACC test 0.888199\n",
      "Validation Accuracy increased (0.844720 --> 0.888199).  Saving model ...\n",
      "Epoch: 4 \tTraining Loss: 0.406229 \tValid Loss: 0.424116 \tACC train 0.904891 \tACC test 0.894410\n",
      "Validation Accuracy increased (0.888199 --> 0.894410).  Saving model ...\n",
      "Epoch: 5 \tTraining Loss: 0.397975 \tValid Loss: 0.416474 \tACC train 0.913043 \tACC test 0.919255\n",
      "Validation Accuracy increased (0.894410 --> 0.919255).  Saving model ...\n",
      "Epoch: 6 \tTraining Loss: 0.381681 \tValid Loss: 0.415738 \tACC train 0.932065 \tACC test 0.919255\n",
      "Validation Accuracy increased (0.919255 --> 0.919255).  Saving model ...\n",
      "Epoch: 7 \tTraining Loss: 0.371627 \tValid Loss: 0.417712 \tACC train 0.937500 \tACC test 0.900621\n",
      "Epoch: 8 \tTraining Loss: 0.365674 \tValid Loss: 0.417419 \tACC train 0.940217 \tACC test 0.888199\n",
      "Epoch: 9 \tTraining Loss: 0.362008 \tValid Loss: 0.410679 \tACC train 0.942935 \tACC test 0.894410\n",
      "Epoch: 10 \tTraining Loss: 0.355471 \tValid Loss: 0.409707 \tACC train 0.956522 \tACC test 0.906832\n",
      "Epoch: 11 \tTraining Loss: 0.351220 \tValid Loss: 0.413687 \tACC train 0.959239 \tACC test 0.913043\n",
      "Epoch: 12 \tTraining Loss: 0.346068 \tValid Loss: 0.409532 \tACC train 0.964674 \tACC test 0.906832\n",
      "Epoch: 13 \tTraining Loss: 0.343375 \tValid Loss: 0.414783 \tACC train 0.967391 \tACC test 0.900621\n",
      "Epoch: 14 \tTraining Loss: 0.341019 \tValid Loss: 0.411221 \tACC train 0.970109 \tACC test 0.913043\n",
      "Epoch: 15 \tTraining Loss: 0.338983 \tValid Loss: 0.410777 \tACC train 0.970109 \tACC test 0.900621\n",
      "Epoch: 16 \tTraining Loss: 0.341574 \tValid Loss: 0.417786 \tACC train 0.964674 \tACC test 0.881988\n",
      "Epoch: 17 \tTraining Loss: 0.338032 \tValid Loss: 0.408096 \tACC train 0.967391 \tACC test 0.906832\n",
      "Epoch: 18 \tTraining Loss: 0.340489 \tValid Loss: 0.413673 \tACC train 0.967391 \tACC test 0.881988\n",
      "Epoch: 19 \tTraining Loss: 0.335298 \tValid Loss: 0.411449 \tACC train 0.972826 \tACC test 0.888199\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.8881987577639752\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9072    0.9072    0.9072        97\n",
      "         1.0     0.8594    0.8594    0.8594        64\n",
      "\n",
      "    accuracy                         0.8882       161\n",
      "   macro avg     0.8833    0.8833    0.8833       161\n",
      "weighted avg     0.8882    0.8882    0.8882       161\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classify(Xphonological, yArousal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/camilo/.local/lib/python3.6/site-packages/ipykernel_launcher.py:15: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 \tTraining Loss: 0.500382 \tValid Loss: 0.430194 \tACC train 0.847826 \tACC test 0.881988\n",
      "Validation Accuracy increased (0.000000 --> 0.881988).  Saving model ...\n",
      "Epoch: 1 \tTraining Loss: 0.361214 \tValid Loss: 0.393376 \tACC train 0.934783 \tACC test 0.925466\n",
      "Validation Accuracy increased (0.881988 --> 0.925466).  Saving model ...\n",
      "Epoch: 2 \tTraining Loss: 0.327578 \tValid Loss: 0.387862 \tACC train 0.986413 \tACC test 0.913043\n",
      "Epoch: 3 \tTraining Loss: 0.316949 \tValid Loss: 0.355853 \tACC train 0.994565 \tACC test 0.962733\n",
      "Validation Accuracy increased (0.925466 --> 0.962733).  Saving model ...\n",
      "Epoch: 4 \tTraining Loss: 0.310851 \tValid Loss: 0.368096 \tACC train 1.000000 \tACC test 0.937888\n",
      "Epoch: 5 \tTraining Loss: 0.310603 \tValid Loss: 0.382773 \tACC train 0.997283 \tACC test 0.925466\n",
      "Epoch: 6 \tTraining Loss: 0.308930 \tValid Loss: 0.384760 \tACC train 1.000000 \tACC test 0.925466\n",
      "Epoch: 7 \tTraining Loss: 0.308863 \tValid Loss: 0.379705 \tACC train 1.000000 \tACC test 0.925466\n",
      "Epoch: 8 \tTraining Loss: 0.308675 \tValid Loss: 0.382512 \tACC train 1.000000 \tACC test 0.925466\n",
      "Epoch: 9 \tTraining Loss: 0.308529 \tValid Loss: 0.383119 \tACC train 1.000000 \tACC test 0.925466\n",
      "Epoch: 10 \tTraining Loss: 0.308704 \tValid Loss: 0.366096 \tACC train 1.000000 \tACC test 0.944099\n",
      "Epoch: 11 \tTraining Loss: 0.308750 \tValid Loss: 0.384797 \tACC train 1.000000 \tACC test 0.931677\n",
      "Epoch: 12 \tTraining Loss: 0.309217 \tValid Loss: 0.377136 \tACC train 1.000000 \tACC test 0.931677\n",
      "Epoch: 13 \tTraining Loss: 0.308596 \tValid Loss: 0.373597 \tACC train 1.000000 \tACC test 0.937888\n",
      "Epoch: 14 \tTraining Loss: 0.308527 \tValid Loss: 0.378416 \tACC train 1.000000 \tACC test 0.931677\n",
      "Epoch: 15 \tTraining Loss: 0.308276 \tValid Loss: 0.378354 \tACC train 1.000000 \tACC test 0.937888\n",
      "Epoch: 16 \tTraining Loss: 0.308301 \tValid Loss: 0.378511 \tACC train 1.000000 \tACC test 0.931677\n",
      "Epoch: 17 \tTraining Loss: 0.308476 \tValid Loss: 0.379392 \tACC train 1.000000 \tACC test 0.931677\n",
      "Epoch: 18 \tTraining Loss: 0.308311 \tValid Loss: 0.387710 \tACC train 1.000000 \tACC test 0.919255\n",
      "Epoch: 19 \tTraining Loss: 0.308314 \tValid Loss: 0.387259 \tACC train 1.000000 \tACC test 0.919255\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.9192546583850931\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9375    0.9278    0.9326        97\n",
      "         1.0     0.8923    0.9062    0.8992        64\n",
      "\n",
      "    accuracy                         0.9193       161\n",
      "   macro avg     0.9149    0.9170    0.9159       161\n",
      "weighted avg     0.9195    0.9193    0.9194       161\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classify(Xrep, yArousal)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## classify positive vs. negative valence with the different feature sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 \tTraining Loss: 0.681314 \tValid Loss: 0.673568 \tACC train 0.494565 \tACC test 0.701863\n",
      "Validation Accuracy increased (0.000000 --> 0.701863).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/camilo/.local/lib/python3.6/site-packages/ipykernel_launcher.py:15: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 \tTraining Loss: 0.671363 \tValid Loss: 0.666985 \tACC train 0.673913 \tACC test 0.708075\n",
      "Validation Accuracy increased (0.701863 --> 0.708075).  Saving model ...\n",
      "Epoch: 2 \tTraining Loss: 0.666432 \tValid Loss: 0.664462 \tACC train 0.695652 \tACC test 0.701863\n",
      "Epoch: 3 \tTraining Loss: 0.661509 \tValid Loss: 0.665339 \tACC train 0.698370 \tACC test 0.652174\n",
      "Epoch: 4 \tTraining Loss: 0.652614 \tValid Loss: 0.662281 \tACC train 0.695652 \tACC test 0.670807\n",
      "Epoch: 5 \tTraining Loss: 0.645642 \tValid Loss: 0.661541 \tACC train 0.679348 \tACC test 0.670807\n",
      "Epoch: 6 \tTraining Loss: 0.631084 \tValid Loss: 0.656243 \tACC train 0.722826 \tACC test 0.677019\n",
      "Epoch: 7 \tTraining Loss: 0.615624 \tValid Loss: 0.654172 \tACC train 0.720109 \tACC test 0.683230\n",
      "Epoch: 8 \tTraining Loss: 0.609962 \tValid Loss: 0.641694 \tACC train 0.739130 \tACC test 0.677019\n",
      "Epoch: 9 \tTraining Loss: 0.590145 \tValid Loss: 0.650743 \tACC train 0.728261 \tACC test 0.677019\n",
      "Epoch: 10 \tTraining Loss: 0.581740 \tValid Loss: 0.633583 \tACC train 0.752717 \tACC test 0.664596\n",
      "Epoch: 11 \tTraining Loss: 0.577279 \tValid Loss: 0.634251 \tACC train 0.744565 \tACC test 0.683230\n",
      "Epoch: 12 \tTraining Loss: 0.573103 \tValid Loss: 0.647685 \tACC train 0.758152 \tACC test 0.689441\n",
      "Epoch: 13 \tTraining Loss: 0.556394 \tValid Loss: 0.632174 \tACC train 0.771739 \tACC test 0.689441\n",
      "Epoch: 14 \tTraining Loss: 0.550905 \tValid Loss: 0.654728 \tACC train 0.760870 \tACC test 0.652174\n",
      "Epoch: 15 \tTraining Loss: 0.549412 \tValid Loss: 0.628913 \tACC train 0.747283 \tACC test 0.683230\n",
      "Epoch: 16 \tTraining Loss: 0.542532 \tValid Loss: 0.645547 \tACC train 0.758152 \tACC test 0.645963\n",
      "Epoch: 17 \tTraining Loss: 0.543429 \tValid Loss: 0.628934 \tACC train 0.774457 \tACC test 0.683230\n",
      "Epoch: 18 \tTraining Loss: 0.555707 \tValid Loss: 0.651539 \tACC train 0.785326 \tACC test 0.664596\n",
      "Epoch: 19 \tTraining Loss: 0.542038 \tValid Loss: 0.639281 \tACC train 0.774457 \tACC test 0.664596\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.6645962732919255\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7685    0.7411    0.7545       112\n",
      "         1.0     0.4528    0.4898    0.4706        49\n",
      "\n",
      "    accuracy                         0.6646       161\n",
      "   macro avg     0.6107    0.6154    0.6126       161\n",
      "weighted avg     0.6724    0.6646    0.6681       161\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classify(Xphonation, yValence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 \tTraining Loss: 0.654075 \tValid Loss: 0.609802 \tACC train 0.703804 \tACC test 0.683230\n",
      "Validation Accuracy increased (0.000000 --> 0.683230).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/camilo/.local/lib/python3.6/site-packages/ipykernel_launcher.py:15: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 \tTraining Loss: 0.547831 \tValid Loss: 0.573420 \tACC train 0.836957 \tACC test 0.745342\n",
      "Validation Accuracy increased (0.683230 --> 0.745342).  Saving model ...\n",
      "Epoch: 2 \tTraining Loss: 0.449804 \tValid Loss: 0.545747 \tACC train 0.902174 \tACC test 0.782609\n",
      "Validation Accuracy increased (0.745342 --> 0.782609).  Saving model ...\n",
      "Epoch: 3 \tTraining Loss: 0.383427 \tValid Loss: 0.543458 \tACC train 0.951087 \tACC test 0.770186\n",
      "Epoch: 4 \tTraining Loss: 0.340624 \tValid Loss: 0.541483 \tACC train 0.978261 \tACC test 0.751553\n",
      "Epoch: 5 \tTraining Loss: 0.328871 \tValid Loss: 0.547033 \tACC train 0.986413 \tACC test 0.757764\n",
      "Epoch: 6 \tTraining Loss: 0.322512 \tValid Loss: 0.540059 \tACC train 0.991848 \tACC test 0.782609\n",
      "Validation Accuracy increased (0.782609 --> 0.782609).  Saving model ...\n",
      "Epoch: 7 \tTraining Loss: 0.316446 \tValid Loss: 0.539279 \tACC train 0.994565 \tACC test 0.776398\n",
      "Epoch: 8 \tTraining Loss: 0.314359 \tValid Loss: 0.538861 \tACC train 0.997283 \tACC test 0.763975\n",
      "Epoch: 9 \tTraining Loss: 0.310324 \tValid Loss: 0.539703 \tACC train 1.000000 \tACC test 0.776398\n",
      "Epoch: 10 \tTraining Loss: 0.310739 \tValid Loss: 0.536583 \tACC train 1.000000 \tACC test 0.782609\n",
      "Validation Accuracy increased (0.782609 --> 0.782609).  Saving model ...\n",
      "Epoch: 11 \tTraining Loss: 0.310630 \tValid Loss: 0.532116 \tACC train 0.997283 \tACC test 0.782609\n",
      "Validation Accuracy increased (0.782609 --> 0.782609).  Saving model ...\n",
      "Epoch: 12 \tTraining Loss: 0.309725 \tValid Loss: 0.534915 \tACC train 1.000000 \tACC test 0.782609\n",
      "Validation Accuracy increased (0.782609 --> 0.782609).  Saving model ...\n",
      "Epoch: 13 \tTraining Loss: 0.309883 \tValid Loss: 0.541265 \tACC train 1.000000 \tACC test 0.770186\n",
      "Epoch: 14 \tTraining Loss: 0.309320 \tValid Loss: 0.539599 \tACC train 1.000000 \tACC test 0.770186\n",
      "Epoch: 15 \tTraining Loss: 0.308996 \tValid Loss: 0.536946 \tACC train 1.000000 \tACC test 0.770186\n",
      "Epoch: 16 \tTraining Loss: 0.309070 \tValid Loss: 0.536280 \tACC train 1.000000 \tACC test 0.770186\n",
      "Epoch: 17 \tTraining Loss: 0.308775 \tValid Loss: 0.536563 \tACC train 1.000000 \tACC test 0.776398\n",
      "Epoch: 18 \tTraining Loss: 0.308694 \tValid Loss: 0.537744 \tACC train 1.000000 \tACC test 0.770186\n",
      "Epoch: 19 \tTraining Loss: 0.308762 \tValid Loss: 0.536886 \tACC train 1.000000 \tACC test 0.776398\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.7763975155279503\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8455    0.8304    0.8378       112\n",
      "         1.0     0.6275    0.6531    0.6400        49\n",
      "\n",
      "    accuracy                         0.7764       161\n",
      "   macro avg     0.7365    0.7417    0.7389       161\n",
      "weighted avg     0.7791    0.7764    0.7776       161\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classify(Xarticulation, yValence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 \tTraining Loss: 0.672757 \tValid Loss: 0.652485 \tACC train 0.633152 \tACC test 0.701863\n",
      "Validation Accuracy increased (0.000000 --> 0.701863).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/camilo/.local/lib/python3.6/site-packages/ipykernel_launcher.py:15: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 \tTraining Loss: 0.637652 \tValid Loss: 0.622449 \tACC train 0.744565 \tACC test 0.689441\n",
      "Epoch: 2 \tTraining Loss: 0.597057 \tValid Loss: 0.609627 \tACC train 0.698370 \tACC test 0.689441\n",
      "Epoch: 3 \tTraining Loss: 0.567202 \tValid Loss: 0.591895 \tACC train 0.725543 \tACC test 0.689441\n",
      "Epoch: 4 \tTraining Loss: 0.528877 \tValid Loss: 0.580324 \tACC train 0.788043 \tACC test 0.683230\n",
      "Epoch: 5 \tTraining Loss: 0.492093 \tValid Loss: 0.577688 \tACC train 0.801630 \tACC test 0.695652\n",
      "Epoch: 6 \tTraining Loss: 0.468022 \tValid Loss: 0.584861 \tACC train 0.831522 \tACC test 0.677019\n",
      "Epoch: 7 \tTraining Loss: 0.439834 \tValid Loss: 0.579124 \tACC train 0.883152 \tACC test 0.714286\n",
      "Validation Accuracy increased (0.701863 --> 0.714286).  Saving model ...\n",
      "Epoch: 8 \tTraining Loss: 0.417330 \tValid Loss: 0.580934 \tACC train 0.907609 \tACC test 0.683230\n",
      "Epoch: 9 \tTraining Loss: 0.404423 \tValid Loss: 0.591209 \tACC train 0.913043 \tACC test 0.683230\n",
      "Epoch: 10 \tTraining Loss: 0.408085 \tValid Loss: 0.603642 \tACC train 0.918478 \tACC test 0.683230\n",
      "Epoch: 11 \tTraining Loss: 0.388892 \tValid Loss: 0.570364 \tACC train 0.913043 \tACC test 0.701863\n",
      "Epoch: 12 \tTraining Loss: 0.376947 \tValid Loss: 0.576472 \tACC train 0.948370 \tACC test 0.714286\n",
      "Validation Accuracy increased (0.714286 --> 0.714286).  Saving model ...\n",
      "Epoch: 13 \tTraining Loss: 0.366304 \tValid Loss: 0.596244 \tACC train 0.956522 \tACC test 0.689441\n",
      "Epoch: 14 \tTraining Loss: 0.370977 \tValid Loss: 0.599881 \tACC train 0.948370 \tACC test 0.689441\n",
      "Epoch: 15 \tTraining Loss: 0.360812 \tValid Loss: 0.594998 \tACC train 0.953804 \tACC test 0.714286\n",
      "Validation Accuracy increased (0.714286 --> 0.714286).  Saving model ...\n",
      "Epoch: 16 \tTraining Loss: 0.357511 \tValid Loss: 0.594873 \tACC train 0.953804 \tACC test 0.714286\n",
      "Validation Accuracy increased (0.714286 --> 0.714286).  Saving model ...\n",
      "Epoch: 17 \tTraining Loss: 0.358073 \tValid Loss: 0.582518 \tACC train 0.953804 \tACC test 0.720497\n",
      "Validation Accuracy increased (0.714286 --> 0.720497).  Saving model ...\n",
      "Epoch: 18 \tTraining Loss: 0.345376 \tValid Loss: 0.588874 \tACC train 0.970109 \tACC test 0.708075\n",
      "Epoch: 19 \tTraining Loss: 0.349352 \tValid Loss: 0.575002 \tACC train 0.959239 \tACC test 0.714286\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAFgCAYAAACFYaNMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzt3Xl8nGW9///XJ8tkmSzN0qZ7Uwp0gbJ1BaFNWSri0RbUoojKUUC0KAKKnuNxwyNfFOGIPxGpRZRFdgQEFBBJa4G2tBRaCq0t3aFLmjT7nrl+f8xMSNuknSQzcyeZ9/PxyKNzZyb3/bk77TvXXPd1Xbc55xARkfhL8roAEZFEpQAWEfGIAlhExCMKYBERjyiARUQ8ogAWEfGIAlhExCMKYBERjyiARUQ8kuJ1AV0pLCx0xcXFXpcBQF1dHX6/3+sy4iJRzlXnOfD0pXNdvXr1fufc4KO9rs8GcHFxMatWrfK6DABKS0spKSnxuoy4SJRz1XkOPH3pXM1seySvUxeEiIhHFMAiIh5RAIuIeKTP9gGLSP/R0tLCrl27aGxs9KyG3Nxc3n333bgeMz09nZEjR5Kamtqjn1cAi0iv7dq1i+zsbIqLizEzT2qoqakhOzs7bsdzzlFeXs6uXbsYO3Zsj/ahLggR6bXGxkYKCgo8C18vmBkFBQW9avUrgEUkKhIpfMN6e84KYBERj6gPWESi7s8rdkR1f5fMGN2t19fW1nLVVVdxxRVXMHv27KjWEk0KYBEZcLKyshg7dizRuOnwrbfeypAhQ6iqquLqq6+OQnUfUheEiAxIycnJvd7HsmXLKC8v5wtf+AIHDhxgxYoVUajsQ2oBi0i/19zczF133UVycjLvvvsuixYtan9u586d3HjjjUyePJnk5GQWLlzInXfeSVZWFk8//TSPPvroYdthzz33HBMnTgRg0qRJPPfcc8yYMSNqdSuA5SAVdc1d9t91tx9OJF4WL17MqaeeyjnnnMPtt99+0HP79u1j/vz5TJ06lUsuuYSFCxfy5JNPsnjxYk4++WSAw7bD9u/fT15eHhCcdLFnz56o1q0uCBHp99atW0dmZiYA11xzzUHPTZ48mU2bNrF+/Xra2toA+MY3vsHs2bN55plnOt0OGzx4MPX19UBwokdBQUFU61YAi0i/d9xxx/HnP/8ZgH/84x80NTW1P7d48WLa2toOGg0xdOhQ3nrrLR544AHKy8sP2w674IILWLt2LQDvvPMO559/flTrVheEiERdvLurrrjiCubNm8dpp53GDTfcgHOOt99+m7S0NKZPn85VV12Fz+ejpqaG9evX87WvfY1vfOMbzJs3j4KCAs4///yDtsM+8pGP8PLLL/OHP/yBQYMGMWvWrKjWrQAWkX4vOzubp5566qC1IB577LH2x5s2bQKCXQ0Ar7/++kE/f+h2R//zP/8TzVIPoi4IERGPKIBFRDyiABYR8YgCWETEIwpgERGPKIBFRDyiYWgiEnUd12KIhiuvvLJbr+8vy1GqBSwiA040l6NcunQp55xzThSqOpwCWEQGpGgsRwkwa9YsGhoaorKvQ6kLQkT6vVgtRxnm8/liUrdawCLS74WXo7z22ms54YQTDnouvBzlxRdfzBNPPAEEl58sKSnhBz/4Qafb8aIAFpF+L1bLUcaaAlhE+r1YLUcZa+oDFpGo6+6wsd6K1XKUEGxdv/fee7z99tuceOKJUa1bASwi/V4sl6OcPHkyO3fujGa57dQFISLikZgFsJnNMrOXQo+TzOxHZnapmX0pVscUEelPYhbAzrmlQEZo8xJgt3PufuB0MxsVq+OKiDeiMeusv+ntOVss/9LMrNQ5V2JmfwbudM79y8yuByqcc/d08vorgSsBioqKpjz00EMxq607amtrycrK8rqMuKisqiaQkt7pc/n+2AxG90KivKfxOs+srCyKiorIzc3FzGJ+vM60tbVFbfZbJJxzVFVVsXfvXmpraw96bs6cOaudc1OPto94XYQrBA6EHjcCQzt7kXNuEbAIYOrUqa6kpCQuxR1NaWkpfaWWWHvi2RdoLJzQ6XMlcb7RYiwlynsar/NsaWlh165dvP/++zE/VlcaGxtJT++88RAr6enpnHzyyaSmpvbo5+MVwGVAZuhxNhC/gXYiEnOpqamMHTvW0xpKS0s59dRTPa2hu+I1CuI54KTQ4+OBf8TpuCIifVYsR0FMBsaZ2YnAQ8AxZvZl4BXn3JZYHVdEpL+IWReEc24d0HG0w3/H6lgiIv2RJmKIiHhEASwi4hEFsIiIRxTAIiIeUQCLiHhEASwi4hEFsIiIRxTAIiIeUQCLiHhEASwi4hEFsIiIRxTAIiIeUQCLiHhEASwi4hEFsIiIRxTAIiIeUQCLiHhEASwi4hEFsIiIRxTAIiIeUQCLiHhEASwi4hEFsIiIRxTAIiIeUQCLiHhEASwi4hEFsIiIRxTAIiIeUQCLiHhEASwi4hEFsIiIRxTAIiIeUQCLiHhEASwi4hEFsIiIRxTAIiIeUQCLiHhEASwi4hEFsIiIRxTAIiIeUQCLiHhEASwi4hEFsIiIRxTAIiIeUQCLiHgkrgFsZplm9lMzu9DMbjaznHgeX0SkL4l3C/ijwH7n3F+AncA5cT6+iEifEe8AXgl8xcyOBbKB5+N8fBGRPsOcc/E9oNlXgM8D9znn7jnkuSuBKwGKioqmPPTQQ3GtrSu1tbVkZWV5XUZcVFZVE0hJ7/S5fL8vztXETqK8p4lyntC3znXOnDmrnXNTj/a6lHgUE2ZmxwAjgY8Bz5vZFufckvDzzrlFwCKAqVOnupKSkniW16XS0lL6Si2x9sSzL9BYOKHT50pmjI5zNbGTKO9popwn9M9zjXcXxClAhXOuCfgVcGqcjy8i0mfEO4CfA0aZ2QXAeOBPcT6+iEifEdcuCOdcI3BDaPO5eB5bRKSv0UQMERGPKIBFRDyiABYR8YgCWETEIwpgERGPKIBFRDyiABYR8YgCWETEIwpgERGPKIBFRDyiABYR8YgCWETEIwpgERGPKIBFRDyiABYR8YgCWETEIwpgERGPKIBFRDyiABYR8YgCWETEIwpgERGPKIBFRDwSUQCb2axDtufHphwRkcSRcqQnzWwU8C1gmpltDn07CZgIPBnj2kREBrQjBrBzbqeZ/QY4A1gW/jawP9aFiYgMdEcMYADn3FYzawJmAb7Qt08Fro1lYSIiA91RAzjkGeBFoD60PTQ25YiIJI6IA9g598PwhpmNjFE9IiIJI9IAHmdmTwCVgAHHALNjVpWISAKINICfBFYDbaHts2JTjohI4og0gDOAM0OPk4BJsSlHRCRxRDoTbirBrgcD8mNXjohI4oi0Bfxt51xzeMPM7olRPSIiCSPSAL7LzFzosZ9gl4SIiPRCpAFcCrxMsAui2Tm3O2YViYgkiEj7gJ8FvgP8AbjOzPJiV5KISGKINID/H/AKcDXwMPC1mFUkIpIgIu2C+Jdz7qHwxqHLU4qISPdFGsAFZvYDoIbgkLTy2JUkIpIYIuqCcM79H7Cc4GpoDzrnrolpVSIiCSDSO2LMBVKcc78A2sxseGzLEhEZ+CK9CPcFYEPo8fPA72JTjohI4og0gF9yzm0NPT4JrQUhItJrkV6EW2Nm9xJchjID+ErsShIRSQwRBbBz7i3gizGuRUQkoUTaBSEiIlEWaRfEQcws2TnXdvRXHvZzBnwJ2Ae85Zx7vyfHFxEZCLoMYDP7PJDcyVPhBdlv6MHxbgbudc6t78HPiogMKOac6/wJs18B7wKNwOeAp/jwrsgznXPdWg/CzM4AbgIeB4YDP+q4xnDoNVcCVwIUFRVNeeihhw7bjxdqa2vJysryuoy4qKyqJpCS3ulz+X5fnKuJnUR5TxPlPKFvneucOXNWO+emHu11RwrgFOdca+jxZw9ZC+IPzrkvd6cgM/s5sN45d6+Z3QVsdM7d1tXrp06d6latWtWdQ8RMaWkpJSUlXpcRF088+wKNhRM6fe6SGaPjXE3sJMp7mijnCX3rXM0sogDusgsiHL4hRWb2M2AvcAbBKcndlQ5Uhx4/A1zYg32IiAwYka4FcTvwVyCVYFfEgh4caxlwauhxKvB6D/YhIjJgRLoWxCeBOcBtBFuxI7t7IOfco4DfzC4ExgB3d3cfIiIDSaTD0OYTnI7sgGfNbAkwu7sHc859u7s/IyIyUEUawM8DBwA6tGBFRKQXIg3gTcD1ZnYrECC4OpqIiPRCpAGc5pz7PICZFQIlMatIRCRBRLoWxMNmdpuZpTnn9gNDYlmUiEgiiDSAfwC8DSwLzWirjF1JIiKJIdIuiHzn3P+Z2T+A3wODgD/HriwRkYEv0hbwe2ZW4Jzb4Zz7KBrDKyLSa0daDW2ec+6p0OYB4ITgapIAZMe6MPFGoIu1QUQk+o7UBdFxtttXga1AeH0I3RNuAHpjxwG+92orXzmzjjEFfq/LERnwjrQYzx0dNq9yztWGN8xsWEyrEk/c++o2WgPw0rv7+PKZY70uR2TAO1IXxP8H+Dtstz8EjgPOjGllEleV9c089/YeslNhc1ktO8rrGK1WsEhMHakLYjnwGtDZrYeOus6l9C9/e3sPza0BrjwpmTvWtvHvfbUKYJEYO1IXxAPhx2aWBAwjeIsiA0bEvjSJp417ashKS2FsDuT5feyrafK6JJEBL9JxwIuBwQRvT9RAcD3fX8eqKIm/rfvrGFOQiVkDQ7LTKKtp9LokkQEv0nHA/wQ+CTzjnPsiUBa7ksQL28rrKC4MdjkMzk5jf20zbQENSROJpUgDeArwfWC/mb0BzIpdSRJvLW0Bdh1oYGyoz3dIdhptAceB+uaj/KSI9EZEXRDOuWvDj81sKZATs4ok7nZW1NMWcMEW8B4YnB28K3JZTROFWWkeVycycEUUwGZ2OfAZgn2/RvC28uNjWJfE0bbyOgDGFmayfU+wBQywr6aJiRrxLRIzkV6E+w9gnnOuEcDMZsauJIm3rfvrARhT4Gc7kJ6aTHZaCvs1EkIkpiLtA/4LB4d1IAa1iEe2l9eRnZZCgd/X/r3czFSqG1s8rEpk4Iu0BXw2sNDMagl2QYwkOBtOBoAPKhsZkZfRcbYjOemp7K9VC1gkliIN4Bbn3PTwhpl1+7b00neV1TYxOPvgi205GSls2V/bxU+ISDRE2gWx0cz+w8xmmdks4LOxLEria39NJwGcnkpjS4DmVvU2icRKpC3gyQSHnoWXozwhNuVIvDnnKOsigAFqGlso0FA0kZiINID3AT9yzgUAzEz/IweI6oZWmtsCDD4kZLMzgv80qhtbFcAiMRJpF0QKwYtvYboAN0CU1QbXfOiqBVzdoJEQIrESaQt4CrDSzKpD2xMIro4m/VxZTXC68aEBnJsRCmANRROJmUgDeKFzbm14w8zUAh4gykJDzYYcEsBpKUmkJptawCIxFGkXRJ2Z/dXM3jOzRwH9rxwgykKz3QZnpR/0fTMjJz2V6sbWzn5MRKIg0gD+DvAz4ETg68CCmFUkcVVW04QvOYmcjMM/DOVkpKoFLBJDkQbwq8655c65BudcGZARy6IkfsJD0DrOggvLSkuhtkktYJFYOWIfsJmZc84BPjNbDNQC04Dn4lGcxF5ZbROF2Z0PM/OnpVDXrAAWiZWjXYS73MyeB14ElgLzgSuBmlgXJvFRVtPEiEHpnT7n9yXT2BKgLeBITjq8hSwivXO0LoixQHHoayjBe8ItJxjEMgBU1jeTl+nr9Dl/WvD3c71awSIxcbQW8P865+oBzOxzwPUE1wX+Z8wrk7iorG8hz3/kAK5raiM7NDFDRKLniAHsnKu34NWZXwLnACXOua1xqUxirrGljYaWtvZJF4fK9CUDqB9YJEaO2AVhZvkE+39HA6eHw9fMhsahNomxqtAQs0GZnQfwhy1gBbBILBytC2IFsAd4FvhMaKiSAR9HY4H7vcr6YAB32Qfc3gJui1tNIonkaAH8C+fc7w/9ppm9E6N6JI7Ct50f1GUXhFrAIrF0tFEQ93f2Tefc6zGoReIs3ALO7aILIjnJyEhN1igIkRg5YgA75xriVYjEX1VDqAXcRRcEBC/E1TWpC0IkFiKdiiwD0Id9wF0PMdNsOJHYUQAnsAP1LfiSk8hITe7yNf60FOrVAhaJCQVwAqtqaCY3M7XThXjC/L5kXYQTiREFcAKrrG85YvcDfNgFEVyTSUSiKe4BbGYTzOzZeB9XDnegvplBGV1fgINgCzjgoLFFt6cXiba4BnDobspzAX88jyudq6xv6XIIWlhmeDacLsSJRF2k94SLlv8EFgMXdfakmV1JcLlLioqKKC0tjV9lR1BbW9tnaommfZX1DE5uOOjcklobSd+/oX07rzHY8m3d9x6lpe/Hu8SYGajv6aES5Tyhf55r3ALYzM4F/hVa4KfT1zjnFgGLAKZOnepKSkriVd4RlZaW0ldqiab6l/7GxHGjKSmZ2P69J559gcbCCe3bqcn1wHscyBhJScmJHlQZGwP1PT1Uopwn9M9zjWcL+AqgKBS+p5jZ951zP4vj8aWDxpY2GlsCXa6EFubXdGSRmIlbADvnLg4/NrNSha+3jrYSWlj7imhakEck6jQMLUGFF+LpaiW0MF9KEqnJphawSAx4EsDOuRIvjisfCk9D7moltI4yfSkKYJEYUAs4QR1tJbSO/GnJ1KsLQiTqFMAJqjLCLggIXojTOGCR6FMAJ6jKCC/CQWg6srogRKJOAZygKiNYCS3M70vWKAiRGFAAJ6jK+mYGHWUltDB/WgrNrQEaWxTCItGkAE5QlfUtEXU/wIf3hgsPXROR6FAAJ6jKhqOvhBbmTwt2U5TXKoBFokkBnKAiWQktTC1gkdhQACeoSBZjDwu3gCvqFMAi0aQATlCVDc1HvBtyR1mhFrC6IESiSwGcgCJdCS0s3ZeMoS4IkWhTACegD29HH1kLOMmMTF8y5eqCEIkqBXACqmwIBmmkLWAI3progAJYJKoUwAnowxZw5AHs96WoBSwSZQrgBNSdldDC/GnJagGLRJkCOAFVhbogIh0FAcEWsIahiUSXAjgBdWcx9jB/WjIH6psJBFysyhJJOArgBFTZ0EJqcnBkQ6QyfSkE3If3khOR3lMAJ6DK+hZyM3wRrYQWFr45Z4XGAotEjQI4AVU1NEe8ElqY36fpyCLRpgBOQJX1Ld3q/4UOLWAFsEjUKIATUHfWAg7LVAtYJOoUwAmoqiHYB9wdagGLRJ8COAGFb0fUHanJSWT6khXAIlGkAE4wza0B6prbut0HDJDv9ymARaJIAZxgqrpxO/pDFSiARaJKAZxgwtOQc7sxDTksTwEsElUK4ATTk2nIYeqCEIkuBXCCaQ/gHnRB5GcqgEWiSQGcYCrDfcDdHIYGkJ/lo6GljYbmtmiXJZKQFMAJprI+3AfcsxYwaD0IkWhRACeYqoYWkgyyQxMruiPfHwxgLcwuEh0K4AQTXAktlaSkyFdCCwsHsG5NJBIdCuAEU9nQ0q07YXSkFrBIdCmAE0xlfXO37obcUYE/DVALWCRaFMAJpqqh+yuhhWWnp5CcZFTUNUW5KpHEpABOMD1ZCzgsKcko8Psor1ULWCQaFMAJJrgSWs/6gAEGZ6dRVqMWsEg0KIATSFvAUd3Y2uM+YAgG8D4FsEhUKIATSHUvVkILG5ylFrBItCiAE8iB0Ay23gTwkJw09tc2EQi4aJUlkrAUwAmkN+tAhA3OSqM14NrDXER6TgGcQKpCK6H1ZB2IsMHZ6QCU1aobQqS3FMAJpDK0GHteL0dBAOoHFokCBXAC6c1i7GFDFMAiURO3ADazbDN71My2mNlv43Vc+VA4gHN6OQwN0FA0kSjo/pqEPTcTuAxwwBozm+acez2Ox094VQ0t7dOJe8qflkKmL1ktYJEoMOfiP5zIzB4HvuWc23nI968ErgQoKiqa8tBDD8W9ts7U1taSlZXldRm9duebjWyrDvDzWZldvqayqppASnqnz4VXQ7thaT3H5CZx1cmdv64/GCjv6dEkynlC3zrXOXPmrHbOTT3a6+LZAgaCXRHAjkPDF8A5twhYBDB16lRXUlIS5+o6V1paSl+ppTcWbVrOyLQAJSVndPmaJ559gcbCCZ0+VzJjNABjNryKM6Ok5PSY1BkPA+U9PZpEOU/on+fqxUW4LwA/9OC4Ca+irrm9FdsbQ3Mz2FPdGIWKRBJbXAPYzOYDTzrnasysKJ7HluA6voVZvQ/g4bnp7K5qxIvuK5GBJJ6jIL4O/B/wtJmtBT4er2MLBAIuai3gYbnpNLcGtDC7SC/FrQ/YOfdbQMPPPFLd2EJbwJEfuqtFbwwblAHA7spGCrN6vz+RRKWJGAlif2gR9eh0QQQD+IOqhl7vSySRKYATREWouyAqXRCDgsPPdlcqgEV6QwGcIML3cSuIQhdEgd+HLyWJ3VUaCSHSGwrgBBHugiiIQheEmTEsN50PFMAivaIAThDhLojerITW0bDcdHVBiPSSAjhBlNc2kZOegi8lOm/58NwMdUGI9JICOEGU1zVTEMUhYyPzMthd1UBzayBq+xRJNArgBBGtSRhhYwr8BBzsOlAftX2KJBoFcILYV9PUvph6NIwpCK6otr1cASzSUwrgBLG3upGinOgtHzmmwA/A9vK6qO1TJNHEfTlKib/65lZqGlsZkhO9FnBhlg+/L5ltagF77s8rdnT53PA41iHdpxZwAthXHZyEUZQdvRawmTGmwK8WsEgvKIATwN7Q2r3R7IIAKC7MVB+wSC8ogBPA3tD924qi2AUBwX7gnQfqaW3TUDSRnlAAJ4B9oRbwkGi3gAsyaWlz7DqgGXEiPaEATgB7qxtJT00iJz2611zHD80BYMOemqjuVyRRKIATwN7qJopy0jHr+e3oO3N8URZmsGFPdVT3K5IoFMAJYG91Y1RHQIRl+lIYW+Bnw261gEV6QgGcAMpqmqI6BrijCcOy1QIW6SEF8ADnnGN3VXRnwXU0YWgO2yvqqWtqjcn+RQYyBfAAV17XTENLGyPzMmKy/wlDs3FOF+JEekJTkQe4nRXBiRKj8zMjen1NZQX/WvbnTp+rfSv/sO9VtyYBw7jzkeeYntt/ZsUdf/zxXpcgogAe6HaEAnhUhAHcXTkpAXJTWtnZkNavAnj//v0sWrTI6zKiYuXWii6fu/SjM+NYiXSXuiAGuPAkiVF5sQlggFHpzexs9OFczA4hMiCpBTzA7ayopzArjQxfcsQ/45yjubGB5qZGWpqbcc5hZpRltZGZmUlGRgZJSR/+7h6d3sTbtZlUtKRQ4NPFOJFIKYAHuB0V9YzK7/oCXFlZGUuXLmXlypWsX7+eVatWs3//ftraDg/Sv4T+TEpKxp+TS05eAYVDR5BVNIbWobPZXpCqAI6TQCDAvn37+OCDD3hrwzZqqytpqK2hubmJ1pbgDVjNklj70hMcd9xxDB8+nGOOOYbx48dzyimnkJ9/eH++xJ8CeIDbeaCe00bntW83NDTw/PPP8+KLL7JkyRLWr18PgM/nY/z48YweM4YhY44jMysHX3oGqT4fZkk4F6C1pZmmxgYa6mqprTpAVcV+1q5YigsEgD/xB38Oqycez+TJkznxxBPJysry6KwHnkAgwPbt21m3bh2bN29m27ZtNDU1tT+flp5BRlYOaenp+NKCQw5dIEBNTQ3Lly/n/fffP+j1xxxzDOeeey5z587l7LPPJi8v77BjSuwpgAew1rYAH1Q28vFJKTzzzDM8/PDDPPXUU9TU1JCVlcVHPvIRPv/5z1NSUsKUKVPw+Xz86YGH+Nfb2yI/RmsLB8r2sONAI5u3bGPjv1exatUqzIzi4mJOOeUUpk+frhZXDzQ3N7Nu3TrWrl3L+vXrqampwcwYNWoUp59+OmPGjGHEiBHsavSR6ut8os2lH51JSUkJgUCADz74gHfffZc1a9bwyiuv8OCDD7Jo0SKSkpKYMWMGn/nMZ/jc5z7H0KFD43ymiUsBPID9a9Vayl5cxP/eUUpddRV5eXksWLCAiy++mJKSElJTU3t9jJSUVAYPG8XkU4dy/8SLmFe4H3/FRtatW8e6dev4y1/+wpNPPsnxxx/PjBkzOO2008jIiM2Y5IEgEAiwadMmli9fzhtvvEFjYyN+v58TTjiByZMnM2nSpMM+WezdWkHAGVsZzB4G0UQqedRxHLvbX5OUlMTIkSMZOXIk5513HgAtLS2sWLGCF154gWeffZbrrruO73znO8ydO5cvfOELzJ8/X+9VjCmAB5jW1laeeeYZ7rjjDv7xj39AUgpzP/4JvvnVr3Deeefh80XvzsgdjUxvJjO5jXfr/Xy6uJji4mI+8YlPUFZWxooVK1ixYgX33nsvDz74INOmTWPWrFkUFxdHfYGg/urAgQMsW7aMV199lYqKCtLT05kyZQozZszg2GOPJTm564uorS6J1YylhkwKqKGAWvaSy0qO5SNlrZR08XOpqamceeaZnHnmmdx44428++673Hfffdx///1ccskl5Ofnc8UVV7Bw4UJGjRoVk/NOdOb66NihqVOnulWrVnldBgClpaWUlJR4XcYRNTQ0cM899/DLX/6SrVu3MmrUKE4699O8lXkaG279LP60yH7XdrcLImz62HxeLs9heVUWXxu1l0GpbQc975xj27ZtvPLKK6xcuZKmpiZGjx7NrFmzmDZtGunpsZkq3ZX8/HwqKroePxsPzjk2btzIkiVLePPNN3HOMXHiRE4//XROOeWUiH5Ztjn4/dYcDpDFZHYwxILrcjS5FNZQTGtyBk9/cxbHF2VHXFcgEKC0tJTf/va3/OUvf8HMuOiii/jWt77FGWec0ePzjbW+9P/UzFY756Ye7XVqAfdzBw4c4Le//S233347ZWVlzJw5k1tvvZVPfOITXPPIWqp3VUUcvr01JbeWFVVZvF6VxXmFVQc9Z2aMHTuWsWPH8qlPfYqVK1eyZMkS7r//fh577DFmzpzJrFmzGDFiRFxq9VJDQwOvvfYaS5YsYc+ePfj9fs455xxmz57N4MGDI96Pc/BMWR4VZDKJne3hC5BmrZzitrE2eRLffHANT199Jr6UyIb9JyUlcfbZZ3P22Wezfft2fvOb37B48WIeffRRzjpxJQDcAAAXFklEQVTrLL7//e8zd+5cfXqJAgVwP/X+++/zq1/9it/97nfU1tZywQUX8N3vfpezzjqr/T/Ght3VTBgaecunt3JSAkzKamBNjZ9pubWHtYLDMjIymD17NrNmzWLLli0sWbKEZcuWUVpayrhx45g1axZTpkyJSh91X7Jr1y6WLFnCihUraGpqori4mMsuu6z9Amh3vVyRw/raTMaxh+FWedjz6dbKf57o4/Y3ariz9D2uOfe4bh9jzJgx3HLLLfz4xz/m7rvv5pZbbuH8889nypQpfP/732fevHkHjQmX7lEA9zObN2/mF7/4BX/84x8JBAJcfPHF3HDDDZx88skHva6xpY1t5fV8fPKwuNZXkl/Fv+vSeX7/IBYMLedIjSQzY9y4cYwbN44FCxbw2muvsXTpUu655x4eeeQRTj/9dGbNmkVRUVH8TiDKWltbeeONN1iyZAmbN28mNTWVqVOnUlJSQnFxcY/3+3qVn+VV2ZyWU0tedVmXrzt1SAr/cVI+d5Ru5lNTRjCyhzMi/X4/3/zmN/nqV7/Kfffdx80338xFF13ECSecwH/9139x8cUXk5KiOOku/erqJ9auXcsll1zC+PHjuffee7n88svZtGkTDzzwwGHhC7B5Xy1tAdd+26B4yUkJMCu/mvca0nm1MvJxwFlZWZx33nn85Cc/4Vvf+hbjx4/nn//8Jz/84Q+57bbbWL16Na2t/WeSR3l5OU8++STf+973uPvuu6mqquLTn/40N998M5dddlmvwved2gxeLM/l+MwG5hZUHfGXHMB/XzCRJIP/97cNPT5mWFpaGpdffjkbNmzggQceAODSSy9lwoQJ/P73vz9orLEcnX5l9XHLly/npptu4q9//StZWVl8+9vf5tprrz3qWM21u4J9sJOGxzeAAabl1LG7yceSA7kkG8zIrT1qSIQlJSUxceJEJk6cSFVVFa+88grLli1j0aJF5OTkMG3aNKZPn86YMWP6XB9kY2Mja9as4bXXXmPjxo2YGZMnT2b27NlMmjQpKh/V367J4K9leYxMb2bekAqSIvgrGD4og6tmj+NX/9jEl06vYPrY3o/JTklJ4ZJLLuGzn/0sTz/9ND/72c+48sor+clPfsJ1113HFVdcQXZ2/Lq/+isFcB/knOOll17ipptu4uWXXyY/P58bb7yRq6++OuIZS8u3lDMkO43igtgtwtPRoStyFbkDlDGSf1YMYl0FXDy6npyU7t2+Pjc3lwsuuIDzzz+f9evXs2zZMpYsWcJLL73EkCFDmDZtGlOmTGH48OHdDuO6ptYuVxHrTkA1NTXxzjvv8Oabb7JmzRqampoYPHgwn/zkJ5kxYwaFhYUR7+tIq5qdOLqQJRXZrKnJYnR6EwuGlpPajTz/6qxxPPL6Tm58Zj1PLzyTpEiSOwJJSUnMnz+fefPm8eKLL3LzzTdz/fXX89Of/pSrr76ab37zm926sJhoFMB9SHNzM4888gi/+tWvWL16NcOHD+e2227jiiuu6Na0Xuccr20p54xxBZ61EpPMMdntZDsNbKGI3+7IYXJ2PTNyayns5noRSUlJTJ48mcmTJ1NXV8eaNWtYuXIlzz33HM8++yx5eXnt05/Hjx8f0yFtzjnKy8vZuHEjb731Fu+88w4tLS1kZmYybdo0Tj/9dMaNG9fjv/c2ZzTio5HU9q9a0lmyI5tWZ0zPrWFOfjXJ3dx9hi+Z735sAtc89CaPrd7FgmnRHddrZsydO5e5c+eycuVKfv7zn/Ozn/2MW2+9lS9/+ctcf/31jB07NqrHHAgUwH1AWVkZd911F3fccQd79uxhwoQJ3HXXXXzpS18iLa3793J7r6yOspomZh5TEINqI2cGxeynyFXRmDOSt2r8vFXjZ2R6Eydn1zPR34AvqXvj0P1+f/vkgaqqqvYZdytWrGDp0qUkJSUxYsSI9iFvxcXFDBkypMsLRAEHAZJIItDpx/m6ujr27dvH9u3b2bx5M5s3b+bAgQNAcCzxWWedxSmnnHLUyRJHUt+WxFs1maxy+dSQDnQsxJFOCydm1TMtt67bv7w6+uTJw7n3te384vmNfGzyULLTYzPKZPr06Tz++ONs2LCBW265hUWLFvG73/2O+fPnc/XVVzN79uw+133kFQWwR8KD3RcvXswTTzxBU1MTH/3oR7nnnnuYO3dur/oLX9tSDsDpHgdwWIa1MLuwijPzalhbk8lbNZk8W5bHC/tzmZTVwEnZ9YxMa464nzgsNze3PYxbW1vZvHkzGzZsYOvWraxcuZKlS5cCwdbZoEGDKCwspLCwEJ8/l/2WS1lLKi0pmVhSCq65gaSmanzN1bxmdTQe2EvZvn3U1Hx4q6VBgwZx7LHHcuyxx3LccccxYsSIXgVJm4PllVm8VplNs0silzrGsg8/TaTRShrNpNNKkjmmD+59v62Z8cP/mMS8O17hjpff43sfm9DrfR7JhAkTuPvuu7nxxhv59a9/zeLFi3n88cc54YQTWLhwIZdeemnC9xMrgONsx44d3H///dx9991s2bKFQYMGcfnll7Nw4UImTpwYlWP89a0PGFvoZ0yc+n8jEe7fTAZOdVBFJh+4PN6uyeWtGj9+GhnLPoo4+Kp+pP2xKSkpTJgwgQkTgqESCATYu3cv27Zto6ysjPLycnbv28+atzfQWF8LbS2H78SMJF8m+9L8pOYOIeeYKYzJy2VoQS75g4vIys3DzHp8EatjH2+tS2Mdo6kjncFUMY69ZFnsRxCcPGoQnzptJH9YtpXPTR/FmAJ/zI85YsQIfv7zn/PjH/+Yhx56iN/85jd8/etf59vf/jaf+tSn+OIXv8icOXN6/AmiP1MAx8GuXbt47LHHeOSRR3jttdcAmDNnDj/96U+58MILo7rgyfbyOlZureA7Hx3fZz/mmcEg6hlEPce73ewjl+0U8jaj2UIjY10ZQ6nsdou4o6SkJIYNG8awYcMob07h1cps9tRmMMLg1Ow6zhwa4I31m2hrbSEQaCPVl0ZKqg9HEpVksodB7CWXGpJpoZkAFaRRgY/OJ5dEyjl4n3z+zTBSaONktjHY4ntD0xvOH8/f397N9x5fxwOXz4jaBbmjycjI4D//8z+57LLLWLFiBffccw8PP/ww9913HyNHjuSSSy7hoosuYtq0aQkzuUMBHANtbW2sXr2av//97/ztb39j+fLlAJxyyincdNNNLFiwgHHjxsXk2I+u2kWSwadOGxmT/UdbigUYzgGGuQPsI4etDGE9o9jKEIrdPgKOiIZadeaDxlRer87indoMks0xLbeWmbm1ZKUESEvLIdXnI/WQGWiGI5868qljvPuAMnJ4n3zeYyhbGEKRq2JEYwsj0jtpQR9Fs0tmAyPYRy751HACu0iz+I9tLspJ54efmMR3H1/HnUveY+GcY+N6fDNj5syZzJw5k9tvv52nn36ae++9l1tvvZVf/OIXDB8+nHnz5jF//nxmzZoV93VC4kkBHAVtbW2sW7eOV199lX/961+8+OKLlJeXY2ZMnTqVG2+8kQULFjB+/PiY1rG3upF7XtnKuROLGJrbv/7RmkER1Qxx1ZSRwxaG8A6j2L2zldMH1TAhq4H0o1ywcw7KW1J4rz6dd+oy2N3kw2cBpufWMiMUvN2RbI6hVDGUKupcGjvJZzd5/OmDZEakNTEtt44J/oaj/oJwDt6ty2A5Q2ghhePYzWj296qF31sLpo7iX5v288sXNnJMoZ+PxXnGZFh6ejoLFixgwYIFVFRU8Nxzz/Hkk0/ypz/9iTvvvJO0tDTOOOMM5syZw5w5c5g+fXrMVvTzggK4m5qbm9mwYQNr165l3bp1vPHGGyxfvpza2loAhg4d2j529bzzzovbGMhAwPHjp9fTEnB8/+PR6Uv2ghkMoZrBoSDemzSM5/bn8fz+QRyT2cio9GayU9pINcemvTU0kUoTqdSQTg0ZNBG8sp9FA+N5n2GukpTqAO9UH3ycs07s3gQVvzUxgd0c6/aSWjCCVVVZPLkvn5zkVqbk1nFiVj3ZhwS8c7Cj0cerldlsbUgnh3pOZRvZ1tjtv5cjjRHuCTPjl585mQ8qG/jGg2u4pbWNC0/19lNTfn4+l156KZdeeikNDQ28/PLLvPTSS7z88sv86Ec/4oc//CE+n4+TTjqJKVOmMHXqVE477TTGjx+P3x/7vuxY0HKUh6ivr2ffvn3s3buXPXv2sHXrVpYuXUpzczPvvfcemzdvbp8S6/P5OOGEEzjjjDPav7yYoRUIOL7/5Ns8uHIH3z1/Al8r6Xn3Rk+Xo4yVacX5vN/k493aDP5dn05Va2dtBoefJrJpIJd6Cqkhw47cRXDWicU9Ps/pY/MJOHivPp2VVX62NwY/bQz2tTA4tYW0JEd9WxIfNPmoaUsmI6mNjwyqIVCxs8fdKT0VviNGV6oaWvjqfatYvqWCi04dwXVzj+/xehGxVFFRwZIlS1i+fDmrVq1i9erVVFV9uOLeqFGjGDJkCDNnzqS4uJhRo0YxatQoRo4cybBhw+K+sFOfXI7SzK4H9gG5zrnfRHv/L7zwAtXV1bS2ttLa2kpbW1v74+bmZurq6qitrT3sq6Kigr1797Jv3772lmxHfr+f4447jokTJ3LRRRe1Two4/vjj+8SKXTsq6vnrWx+wcM44rpp9jNflRJVZcLH3kenNnEcVDW1GfVsyzc5Y/341Plrw0Rr3YEsyOM7fyHH+RvY3p7CxLp2djWnsbvLR5Iz0pAAj0ps4LrOR8f5GfEmOlQfiW2MkcjNSuffLM/jNPzfx29L3eOqtD5g6Jo/JI3Lxp6Wwu6qBFVsruP8rMxiV710w5+fnc+GFF3LhhRcCwVEuW7Zs4c0332Tjxo1s2LCBVatWcd9991FdXX3Yz2dnZ5Ofn09BQUH7n3l5efj9ftLT08nIyCAjI6P9cVpaGsnJySQnJzN//vyYLTQUtwA2szOBAufcrWb2AzOb4ZxbEc1jLFy4kM2bNx/xNUlJSWRlZR30lZeXx8yZMykqKmLIkCEH/Tl27FjWrl3LnDlzollqVBUX+nn+2lkMz03vsyMfoiUj2ZGRHPwEstMaPK4mqNDXSqGvFjj8l3d/4EtJ4rq54/ns9NH8ecUOSv+9j/tXbKexJUBeZirTivNpbOnd6I9oS0pKah+THRZekL2qqoqdO3e2f+3du5fy8nIqKirav3bs2EFFRQUNDQ00NDQQCHR9faChoSFmARy3Lggzuwl41zl3n5l9CjjJOfejQ15zJXBlaHM8sDEuxR1dIbDf6yLiJFHOVec58PSlcx3jnDvqBaB4dkEUAuEPYY3AYct5OecWAYviWFNEzGxVJP05A0GinKvOc+Dpj+caz9HOZUC4EykbKI/jsUVE+px4BvBzwEmhx5OAv8fx2CIifU7cAtg59wrQaGZfBiqdc0vjdewo6HPdIjGUKOeq8xx4+t259tlxwCIiA11irHghItIHKYBFRDyiABYR8YgCuAtmNsTMbjSzr5vZGZ08X2xmH5jZHjM714sae8vMrjezL5jZ1Yd8//jQbMXrzex4r+qLlq7OM/TcX0Lv4e+9qC2azGyWmb3UyffPMLPvmNl3zWyIF7VF2xHONcnMVobe0x96UVu3OOf01ckXsBg4PvT4r4QuWHZ4/rtAqtd19uL8zgRuCj3+ATCjw3N/A7KANOAJr2uN4XlOAz7qdY1RPt9XO/neEoI3mhsN3Ol1jTE+108DE7yuLdIvtYC7NhfY1GG7OPzAzHzAR4HtZvb5ONcVLRcA74YevxPaxswygHHOuVrnXBMw1sz687KlnZ5nyBxgsZn9ycz63hJgPdPccSP0CabVBe0AzvKmrJho7uR7HwFeNrNfmlmfz7c+X2A8mNl/m9kfO34Bg13oVyqHTJ12zjU7584GpgM39NOP6V1NDc8DOi4n1QrEZ1Hj2OhyCrxz7hfAWILrB3wv/qXFRcfzh+D7O2A5564FxgEjgcu8rebo+nPLJmqcczcd+j0z+0iHzU6nTjvndpnZz4ATgX/HrsKY6GpqeDnQ8XYamUBlHOuKtiNOgXfOtZrZd4F74l1YnHQ8f4DY3/nTY865ejP7JvA/XtdyNGoBd63UzMJr3aU55/5tZtlmlgxgH677mAEs96TC3jl0avjzZpYb6nbYbmaZZpYO7HTO9Y11H3um0/OEg97DbGCZB7XFjJklm1m2c24ToV+oZnYMUOppYTEQPtfQ4/B7WgAcdpGur9FMuC6Y2XDgG8AeYI1zbqmZ3ULwH3AF8GvgMeA117+mVbczs/8BPgAGEfzH+j3n3OfM7ESCFzOagKecc+94WGavHeE8XwFWAm8Df3TO9a1Fb7vJzCYT/IXzMYLXLM5yzn3XzM4GphJsLCxyzu32rsro6OxcgZ8ArwHPAG875x70rMAIKYBFRDyiLggREY8ogEVEPKIAFhHxiAJYRMQjCmAREY8ogCUmzOwGM/vvHv7syWZ2W4ftS0LTha8xs2/1dn89rGmYmf3DzIpjvQ8zSzGz28zsi4d8/7/N7L96enzpezQTTnrFzMYDVxBcb8EBs4EfAavp4boDzrm3zOw7of1PJ7gw0NeAUwmO84y0ttHOuR0d99cT4f2YWVlP9wHgnNsdyT5Cs/PWc3gD6Q1gZm9qkL5FASw9ZmaFwKMEB/xXhb4XXnegV5MaOkyKOB5ods7VA690o7ZjgKuB6w7ZX7eY2YVADvAnojONN9J9dFZvYxSOL32IAlh644vA6+HwDXmS4DKWxRBcn5XgzRKXAKc7575uZvMAP8GW88cIrizXcfsaIJng+gxnh/azHvgc8KZz7t7Qx/MAcAnwWeAiggvtjAFuAc4ApptZCTADSHbO3WRmCwi2qE8F7gNqQvU9DHwe+F/n3LOhYyaHaksxsxWh85trZh8n2ML/P+B3wBbgE8DpoRpzCa4PcjlwFVALfNI595lD9+Gcu9HMTgLOJbhmwx7n3OKOf8lmdhnBRZE+xsEr9Ek/pz5g6Y3jgYOmtYaWPezYUssg2D3xMB9+fL4A2Al8nWBL79Dt1YAvNGV2KbDUOfc6weUkk8zsBGCYc+5+gi3TTIKrfP0G2EZwScJlwGbnXGl4f6E1IC5zzv0BuBm42zn3bujnfw/8FJjX4VzaCK7zscw5tyH07eUEQ/ZzzrkaguH7JjCFYOAmh863meBKZPMJTl//aYe/k/Z9hLZ/DfzaOfe/wDVmNiz8QjMbDZwTOtcHDn0DpH9TAEtv7AKKjvQC51wdsJdgEIU/ft8W+voxwU9hh263drG78PdPBBpC+3/YObcHeB74CsGFZ5K7+LkJ4cfOufeBwtDiLTWhRYjqAd+RzgeoDnWHhFeMawPKQ2E9CdjgnPu7c+5rBFde+/8Itv7/4wj7mOicC9f4LsFfbGHT+XAFt/qj1Cb9jAJYeuNPBD9Ot9/mxsyOPaQFdwJwsXPuEaA1FHjZBIOlgeDH6kO3j2YT8DkzSzWz0aGFWf4I3E/wlwIELwjaIYtybwYmhW5bkwRs7LDmc1c62w8E7zBxqM3AdaHVuWaFzmsPcDLweTMr6GIfOzusKZ0CrO/wmg8Idm2E6f/sAKI3U3rMObcT+BLBu0r81MyuJNg1sJvg6luTCfZ/TjKzmwgG7IXAtQQvkO0h2FVw6PZU4AQz8xPsTjgrFF6nAqcBbxEcDbEeuMo5t45gN8B9BBfjPhfYR7BFOj+8P4ItyJsItravAb5tZscBx4b+nAKMM7OOi5a/Q3Bh75MJtkxnmNkUIM/MJoXO8dzQL5a7CHa5bAaODfWN3xk656cI9lkfuo9jCPYT/8DMrgAeJ9jiPS1U90pgjZk9QbCrZqyZZXX3vZK+SauhiYh4RC1gERGPKIBFRDyiABYR8YgCWETEIwpgERGPKIBFRDyiABYR8cj/DxI2PbfSgMAnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.7142857142857143\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7845    0.8125    0.7982       112\n",
      "         1.0     0.5333    0.4898    0.5106        49\n",
      "\n",
      "    accuracy                         0.7143       161\n",
      "   macro avg     0.6589    0.6511    0.6544       161\n",
      "weighted avg     0.7080    0.7143    0.7107       161\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classify(Xprosody, yValence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 \tTraining Loss: 0.666929 \tValid Loss: 0.645199 \tACC train 0.567935 \tACC test 0.714286\n",
      "Validation Accuracy increased (0.000000 --> 0.714286).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/camilo/.local/lib/python3.6/site-packages/ipykernel_launcher.py:15: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 \tTraining Loss: 0.618307 \tValid Loss: 0.605402 \tACC train 0.730978 \tACC test 0.720497\n",
      "Validation Accuracy increased (0.714286 --> 0.720497).  Saving model ...\n",
      "Epoch: 2 \tTraining Loss: 0.578396 \tValid Loss: 0.582278 \tACC train 0.739130 \tACC test 0.714286\n",
      "Epoch: 3 \tTraining Loss: 0.536781 \tValid Loss: 0.562184 \tACC train 0.798913 \tACC test 0.745342\n",
      "Validation Accuracy increased (0.720497 --> 0.745342).  Saving model ...\n",
      "Epoch: 4 \tTraining Loss: 0.507895 \tValid Loss: 0.545942 \tACC train 0.828804 \tACC test 0.745342\n",
      "Validation Accuracy increased (0.745342 --> 0.745342).  Saving model ...\n",
      "Epoch: 5 \tTraining Loss: 0.482157 \tValid Loss: 0.554347 \tACC train 0.858696 \tACC test 0.739130\n",
      "Epoch: 6 \tTraining Loss: 0.458936 \tValid Loss: 0.542739 \tACC train 0.853261 \tACC test 0.751553\n",
      "Validation Accuracy increased (0.745342 --> 0.751553).  Saving model ...\n",
      "Epoch: 7 \tTraining Loss: 0.432011 \tValid Loss: 0.547776 \tACC train 0.899457 \tACC test 0.739130\n",
      "Epoch: 8 \tTraining Loss: 0.416783 \tValid Loss: 0.554530 \tACC train 0.918478 \tACC test 0.732919\n",
      "Epoch: 9 \tTraining Loss: 0.398166 \tValid Loss: 0.565109 \tACC train 0.921196 \tACC test 0.726708\n",
      "Epoch: 10 \tTraining Loss: 0.395409 \tValid Loss: 0.569378 \tACC train 0.932065 \tACC test 0.708075\n",
      "Epoch: 11 \tTraining Loss: 0.387685 \tValid Loss: 0.565826 \tACC train 0.915761 \tACC test 0.695652\n",
      "Epoch: 12 \tTraining Loss: 0.368127 \tValid Loss: 0.574933 \tACC train 0.942935 \tACC test 0.720497\n",
      "Epoch: 13 \tTraining Loss: 0.364917 \tValid Loss: 0.552053 \tACC train 0.951087 \tACC test 0.732919\n",
      "Epoch: 14 \tTraining Loss: 0.354376 \tValid Loss: 0.567911 \tACC train 0.964674 \tACC test 0.708075\n",
      "Epoch: 15 \tTraining Loss: 0.353471 \tValid Loss: 0.562868 \tACC train 0.953804 \tACC test 0.745342\n",
      "Epoch: 16 \tTraining Loss: 0.356058 \tValid Loss: 0.556343 \tACC train 0.948370 \tACC test 0.739130\n",
      "Epoch: 17 \tTraining Loss: 0.344994 \tValid Loss: 0.565668 \tACC train 0.970109 \tACC test 0.739130\n",
      "Epoch: 18 \tTraining Loss: 0.327872 \tValid Loss: 0.552437 \tACC train 0.980978 \tACC test 0.751553\n",
      "Validation Accuracy increased (0.751553 --> 0.751553).  Saving model ...\n",
      "Epoch: 19 \tTraining Loss: 0.337828 \tValid Loss: 0.565048 \tACC train 0.967391 \tACC test 0.732919\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.7329192546583851\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7949    0.8304    0.8122       112\n",
      "         1.0     0.5682    0.5102    0.5376        49\n",
      "\n",
      "    accuracy                         0.7329       161\n",
      "   macro avg     0.6815    0.6703    0.6749       161\n",
      "weighted avg     0.7259    0.7329    0.7287       161\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classify(Xphonological, yValence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/camilo/.local/lib/python3.6/site-packages/ipykernel_launcher.py:15: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 \tTraining Loss: 0.641576 \tValid Loss: 0.577312 \tACC train 0.633152 \tACC test 0.745342\n",
      "Validation Accuracy increased (0.000000 --> 0.745342).  Saving model ...\n",
      "Epoch: 1 \tTraining Loss: 0.495647 \tValid Loss: 0.542601 \tACC train 0.845109 \tACC test 0.770186\n",
      "Validation Accuracy increased (0.745342 --> 0.770186).  Saving model ...\n",
      "Epoch: 2 \tTraining Loss: 0.403189 \tValid Loss: 0.497890 \tACC train 0.923913 \tACC test 0.801242\n",
      "Validation Accuracy increased (0.770186 --> 0.801242).  Saving model ...\n",
      "Epoch: 3 \tTraining Loss: 0.352646 \tValid Loss: 0.479816 \tACC train 0.961957 \tACC test 0.844720\n",
      "Validation Accuracy increased (0.801242 --> 0.844720).  Saving model ...\n",
      "Epoch: 4 \tTraining Loss: 0.338053 \tValid Loss: 0.498315 \tACC train 0.978261 \tACC test 0.801242\n",
      "Epoch: 5 \tTraining Loss: 0.329719 \tValid Loss: 0.476651 \tACC train 0.983696 \tACC test 0.832298\n",
      "Epoch: 6 \tTraining Loss: 0.325157 \tValid Loss: 0.468913 \tACC train 0.989130 \tACC test 0.838509\n",
      "Epoch: 7 \tTraining Loss: 0.320663 \tValid Loss: 0.474783 \tACC train 0.991848 \tACC test 0.832298\n",
      "Epoch: 8 \tTraining Loss: 0.317577 \tValid Loss: 0.471041 \tACC train 0.994565 \tACC test 0.838509\n",
      "Epoch: 9 \tTraining Loss: 0.322146 \tValid Loss: 0.478938 \tACC train 0.989130 \tACC test 0.826087\n",
      "Epoch: 10 \tTraining Loss: 0.317246 \tValid Loss: 0.471232 \tACC train 0.994565 \tACC test 0.838509\n",
      "Epoch: 11 \tTraining Loss: 0.317083 \tValid Loss: 0.476907 \tACC train 0.994565 \tACC test 0.838509\n",
      "Epoch: 12 \tTraining Loss: 0.315969 \tValid Loss: 0.468549 \tACC train 0.994565 \tACC test 0.826087\n",
      "Epoch: 13 \tTraining Loss: 0.315577 \tValid Loss: 0.476089 \tACC train 0.994565 \tACC test 0.844720\n",
      "Validation Accuracy increased (0.844720 --> 0.844720).  Saving model ...\n",
      "Epoch: 14 \tTraining Loss: 0.313115 \tValid Loss: 0.472956 \tACC train 0.997283 \tACC test 0.838509\n",
      "Epoch: 15 \tTraining Loss: 0.313198 \tValid Loss: 0.475097 \tACC train 0.997283 \tACC test 0.838509\n",
      "Epoch: 16 \tTraining Loss: 0.313979 \tValid Loss: 0.483317 \tACC train 0.997283 \tACC test 0.819876\n",
      "Epoch: 17 \tTraining Loss: 0.313730 \tValid Loss: 0.487255 \tACC train 0.997283 \tACC test 0.807453\n",
      "Epoch: 18 \tTraining Loss: 0.314343 \tValid Loss: 0.480553 \tACC train 0.994565 \tACC test 0.832298\n",
      "Epoch: 19 \tTraining Loss: 0.313876 \tValid Loss: 0.486437 \tACC train 0.997283 \tACC test 0.826087\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.8260869565217391\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8621    0.8929    0.8772       112\n",
      "         1.0     0.7333    0.6735    0.7021        49\n",
      "\n",
      "    accuracy                         0.8261       161\n",
      "   macro avg     0.7977    0.7832    0.7897       161\n",
      "weighted avg     0.8229    0.8261    0.8239       161\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classify(Xrep, yValence)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
